GcatWiki - User contributions [en]

File:ReadingListImage360x270.png

2017-01-25T12:15:46Z

WikiSysop:

Main Page

2016-06-13T03:02:22Z

WikiSysop:

To request GcatWiki write Access Contact [http://gcat.davidson.edu/WikiAccess/GcatWikiAccessRequest.php Dr Malcolm Campbell]
 

==[http://gcat.davidson.edu/Gcatwiki/index.php/Davidson_Protocols Davidson Protocols]==
 

 

==[http://gcat.davidson.edu/Gcatwiki/index.php/MWSU_protocols MWSU Protocols]==
 

 
==[[Summer 2014 SynBio Project (Davidson and MWSU)]]==
 

==[[Ethics and Philosophy of SynBio]]==
 

 
==[[Summer 2013 SynBio Project (Davidson and MWSU)]]==
 
 

==[[Education Research by Caylyn Harvey]]==
 

==[[Burmese Python RNAseq Project]]==

 
==[[iRobot Energy Saver Project]]==

 
==[[Summer 2012 SynBio Project (Davidson and MWSU)]]==

 
==[[Synthetic Biology Network Research]]==

 
==[[Genome Assembly Project: Leland Taylor '12]]==
 

==[[Blueberry Genome Project for Bio343]]==

 

== [[Halomicrobium mukohataei Genome Fall 2009]] ==

 
== [[Halorhabdus utahensis Genome]] ==

 
==[[Network Research with Synthetic Biology]]==

 
== [[Missouri Western/Davidson SynBio 2011]] ==

 
== [[Missouri Western/Davidson iGEM2010]] ==

 
== [[Missouri Western/Davidson iGEM2009]] ==

 
== [[Davidson/Missouri Western iGEM2008]] ==

 
== [[MAGIC Tool Development]] ==

 
== [[Nova Southeastern University]] ==

 

== [[Davidson College]]== Small liberal arts college near Charlotte, NC.
A. Malcolm Campbell and Laurie J. Heyer GCAT faculty

* [http://gcat.davidson.edu/GcatWiki/index.php/User:Kahaynes Karmella A. Haynes], Visiting Assitant Professor of Biology

* [[A_Review_of_Synthetic_Biology |A Review of Synthetic Biology]] - Davidson College Synthetic Biology Seminar (Fall 2007)

* [[Laboratory Notebooks]]

* [[2009-2010 Biology Curriculum Wiki]]

* [[Team 5: Information Technology Initiatives]]

* [[Biological Noise and Possible Uses]]

* [[New Intro Bio Approach]]

== [[Swarthmore College]]==

 
 
 
----
----
 
Please see [http://meta.wikipedia.org/wiki/MediaWiki_i18n documentation on customizing the interface]
and the [http://meta.wikipedia.org/wiki/MediaWiki_User%27s_Guide User's Guide] for usage and configuration help.
 
 
[http://www.bio.davidson.edu/GCAT GCAT Main Page]

http://parts.mit.edu/igem07/index.php/Duke/Projects/bc - bacterial communication with light.

http://parts.mit.edu/igem07/index.php/Cambridge - they talk a little about making a bacterial internet, I have no idea what they mean.

http://parts.mit.edu/igem07/index.php/Tokyo_Tech - They say, “Bistability and cell-cell communication are necessary to realize our model of ‘Balanced differentiation’.”

Main Page

2016-06-13T03:01:09Z

WikiSysop:

To request GcatWiki write Access Contact [http://gcat.davidson.edu/WikiAccess/GcatWikiAccessRequest.php Dr Malcolm Campbell]
 

==[gcat.davidson.edu/Gcatwiki/index.php/Davidson_Protocols Davidson Protocols]==
 

 

==[http://gcat.davidson.edu/Gcatwiki/index.php/MWSU_protocols MWSU Protocols]==
 

 
==[[Summer 2014 SynBio Project (Davidson and MWSU)]]==
 

==[[Ethics and Philosophy of SynBio]]==
 

 
==[[Summer 2013 SynBio Project (Davidson and MWSU)]]==
 
 

==[[Education Research by Caylyn Harvey]]==
 

==[[Burmese Python RNAseq Project]]==

 
==[[iRobot Energy Saver Project]]==

 
==[[Summer 2012 SynBio Project (Davidson and MWSU)]]==

 
==[[Synthetic Biology Network Research]]==

 
==[[Genome Assembly Project: Leland Taylor '12]]==
 

==[[Blueberry Genome Project for Bio343]]==

 

== [[Halomicrobium mukohataei Genome Fall 2009]] ==

 
== [[Halorhabdus utahensis Genome]] ==

 
==[[Network Research with Synthetic Biology]]==

 
== [[Missouri Western/Davidson SynBio 2011]] ==

 
== [[Missouri Western/Davidson iGEM2010]] ==

 
== [[Missouri Western/Davidson iGEM2009]] ==

 
== [[Davidson/Missouri Western iGEM2008]] ==

 
== [[MAGIC Tool Development]] ==

 
== [[Nova Southeastern University]] ==

 

== [[Davidson College]]== Small liberal arts college near Charlotte, NC.
A. Malcolm Campbell and Laurie J. Heyer GCAT faculty

* [http://gcat.davidson.edu/GcatWiki/index.php/User:Kahaynes Karmella A. Haynes], Visiting Assitant Professor of Biology

* [[A_Review_of_Synthetic_Biology |A Review of Synthetic Biology]] - Davidson College Synthetic Biology Seminar (Fall 2007)

* [[Laboratory Notebooks]]

* [[2009-2010 Biology Curriculum Wiki]]

* [[Team 5: Information Technology Initiatives]]

* [[Biological Noise and Possible Uses]]

* [[New Intro Bio Approach]]

== [[Swarthmore College]]==

 
 
 
----
----
 
Please see [http://meta.wikipedia.org/wiki/MediaWiki_i18n documentation on customizing the interface]
and the [http://meta.wikipedia.org/wiki/MediaWiki_User%27s_Guide User's Guide] for usage and configuration help.
 
 
[http://www.bio.davidson.edu/GCAT GCAT Main Page]

http://parts.mit.edu/igem07/index.php/Duke/Projects/bc - bacterial communication with light.

http://parts.mit.edu/igem07/index.php/Cambridge - they talk a little about making a bacterial internet, I have no idea what they mean.

http://parts.mit.edu/igem07/index.php/Tokyo_Tech - They say, “Bistability and cell-cell communication are necessary to realize our model of ‘Balanced differentiation’.”

Main Page

2016-06-13T02:58:23Z

WikiSysop: /* [gcat.davidson.edu/Gcatwiki/index.php/Davidson_Protocols Davidson Protocols] */

To request GcatWiki write Access Contact [http://gcat.davidson.edu/WikiAccess/GcatWikiAccessRequest.php Dr Malcolm Campbell]
 
 
==[gcat.davidson.edu/Gcatwiki/index.php/Davidson_Protocols Davidson Protocols]==
 

 

==[http://gcat.davidson.edu/Gcatwiki/index.php/MWSU_protocols MWSU Protocols]==
 

 
==[[Summer 2014 SynBio Project (Davidson and MWSU)]]==
 

==[[Ethics and Philosophy of SynBio]]==
 

 
==[[Summer 2013 SynBio Project (Davidson and MWSU)]]==
 
 

==[[Education Research by Caylyn Harvey]]==
 

==[[Burmese Python RNAseq Project]]==

 
==[[iRobot Energy Saver Project]]==

 
==[[Summer 2012 SynBio Project (Davidson and MWSU)]]==

 
==[[Synthetic Biology Network Research]]==

 
==[[Genome Assembly Project: Leland Taylor '12]]==
 

==[[Blueberry Genome Project for Bio343]]==

 

== [[Halomicrobium mukohataei Genome Fall 2009]] ==

 
== [[Halorhabdus utahensis Genome]] ==

 
==[[Network Research with Synthetic Biology]]==

 
== [[Missouri Western/Davidson SynBio 2011]] ==

 
== [[Missouri Western/Davidson iGEM2010]] ==

 
== [[Missouri Western/Davidson iGEM2009]] ==

 
== [[Davidson/Missouri Western iGEM2008]] ==

 
== [[MAGIC Tool Development]] ==

 
== [[Nova Southeastern University]] ==

 

== [[Davidson College]]== Small liberal arts college near Charlotte, NC.
A. Malcolm Campbell and Laurie J. Heyer GCAT faculty

* [http://gcat.davidson.edu/GcatWiki/index.php/User:Kahaynes Karmella A. Haynes], Visiting Assitant Professor of Biology

* [[A_Review_of_Synthetic_Biology |A Review of Synthetic Biology]] - Davidson College Synthetic Biology Seminar (Fall 2007)

* [[Laboratory Notebooks]]

* [[2009-2010 Biology Curriculum Wiki]]

* [[Team 5: Information Technology Initiatives]]

* [[Biological Noise and Possible Uses]]

* [[New Intro Bio Approach]]

== [[Swarthmore College]]==

 
 
 
----
----
 
Please see [http://meta.wikipedia.org/wiki/MediaWiki_i18n documentation on customizing the interface]
and the [http://meta.wikipedia.org/wiki/MediaWiki_User%27s_Guide User's Guide] for usage and configuration help.
 
 
[http://www.bio.davidson.edu/GCAT GCAT Main Page]

http://parts.mit.edu/igem07/index.php/Duke/Projects/bc - bacterial communication with light.

http://parts.mit.edu/igem07/index.php/Cambridge - they talk a little about making a bacterial internet, I have no idea what they mean.

http://parts.mit.edu/igem07/index.php/Tokyo_Tech - They say, “Bistability and cell-cell communication are necessary to realize our model of ‘Balanced differentiation’.”

Main Page

2016-06-13T02:56:03Z

WikiSysop:

To request GcatWiki write Access Contact [http://gcat.davidson.edu/WikiAccess/GcatWikiAccessRequest.php Dr Malcolm Campbell]
 
==[gcat.davidson.edu/Gcatwiki/index.php/Davidson_Protocols Davidson Protocols]==
 

 

==[http://gcat.davidson.edu/Gcatwiki/index.php/MWSU_protocols MWSU Protocols]==
 

 
==[[Summer 2014 SynBio Project (Davidson and MWSU)]]==
 

==[[Ethics and Philosophy of SynBio]]==
 

 
==[[Summer 2013 SynBio Project (Davidson and MWSU)]]==
 
 

==[[Education Research by Caylyn Harvey]]==
 

==[[Burmese Python RNAseq Project]]==

 
==[[iRobot Energy Saver Project]]==

 
==[[Summer 2012 SynBio Project (Davidson and MWSU)]]==

 
==[[Synthetic Biology Network Research]]==

 
==[[Genome Assembly Project: Leland Taylor '12]]==
 

==[[Blueberry Genome Project for Bio343]]==

 

== [[Halomicrobium mukohataei Genome Fall 2009]] ==

 
== [[Halorhabdus utahensis Genome]] ==

 
==[[Network Research with Synthetic Biology]]==

 
== [[Missouri Western/Davidson SynBio 2011]] ==

 
== [[Missouri Western/Davidson iGEM2010]] ==

 
== [[Missouri Western/Davidson iGEM2009]] ==

 
== [[Davidson/Missouri Western iGEM2008]] ==

 
== [[MAGIC Tool Development]] ==

 
== [[Nova Southeastern University]] ==

 

== [[Davidson College]]== Small liberal arts college near Charlotte, NC.
A. Malcolm Campbell and Laurie J. Heyer GCAT faculty

* [http://gcat.davidson.edu/GcatWiki/index.php/User:Kahaynes Karmella A. Haynes], Visiting Assitant Professor of Biology

* [[A_Review_of_Synthetic_Biology |A Review of Synthetic Biology]] - Davidson College Synthetic Biology Seminar (Fall 2007)

* [[Laboratory Notebooks]]

* [[2009-2010 Biology Curriculum Wiki]]

* [[Team 5: Information Technology Initiatives]]

* [[Biological Noise and Possible Uses]]

* [[New Intro Bio Approach]]

== [[Swarthmore College]]==

 
 
 
----
----
 
Please see [http://meta.wikipedia.org/wiki/MediaWiki_i18n documentation on customizing the interface]
and the [http://meta.wikipedia.org/wiki/MediaWiki_User%27s_Guide User's Guide] for usage and configuration help.
 
 
[http://www.bio.davidson.edu/GCAT GCAT Main Page]

http://parts.mit.edu/igem07/index.php/Duke/Projects/bc - bacterial communication with light.

http://parts.mit.edu/igem07/index.php/Cambridge - they talk a little about making a bacterial internet, I have no idea what they mean.

http://parts.mit.edu/igem07/index.php/Tokyo_Tech - They say, “Bistability and cell-cell communication are necessary to realize our model of ‘Balanced differentiation’.”

Main Page

2016-06-13T02:47:46Z

WikiSysop: /* Davidson Protocols */

To request GcatWiki write Access Contact [http://gcat.davidson.edu/WikiAccrss/GcatWikiAccessRequest.php Dr Malcolm Campbell]
 
==[gcat.davidson.edu/Gcatwiki/index.php/Davidson_Protocols Davidson Protocols]==
 

 

==[http://gcat.davidson.edu/Gcatwiki/index.php/MWSU_protocols MWSU Protocols]==
 

 
==[[Summer 2014 SynBio Project (Davidson and MWSU)]]==
 

==[[Ethics and Philosophy of SynBio]]==
 

 
==[[Summer 2013 SynBio Project (Davidson and MWSU)]]==
 
 

==[[Education Research by Caylyn Harvey]]==
 

==[[Burmese Python RNAseq Project]]==

 
==[[iRobot Energy Saver Project]]==

 
==[[Summer 2012 SynBio Project (Davidson and MWSU)]]==

 
==[[Synthetic Biology Network Research]]==

 
==[[Genome Assembly Project: Leland Taylor '12]]==
 

==[[Blueberry Genome Project for Bio343]]==

 

== [[Halomicrobium mukohataei Genome Fall 2009]] ==

 
== [[Halorhabdus utahensis Genome]] ==

 
==[[Network Research with Synthetic Biology]]==

 
== [[Missouri Western/Davidson SynBio 2011]] ==

 
== [[Missouri Western/Davidson iGEM2010]] ==

 
== [[Missouri Western/Davidson iGEM2009]] ==

 
== [[Davidson/Missouri Western iGEM2008]] ==

 
== [[MAGIC Tool Development]] ==

 
== [[Nova Southeastern University]] ==

 

== [[Davidson College]]== Small liberal arts college near Charlotte, NC.
A. Malcolm Campbell and Laurie J. Heyer GCAT faculty

* [http://gcat.davidson.edu/GcatWiki/index.php/User:Kahaynes Karmella A. Haynes], Visiting Assitant Professor of Biology

* [[A_Review_of_Synthetic_Biology |A Review of Synthetic Biology]] - Davidson College Synthetic Biology Seminar (Fall 2007)

* [[Laboratory Notebooks]]

* [[2009-2010 Biology Curriculum Wiki]]

* [[Team 5: Information Technology Initiatives]]

* [[Biological Noise and Possible Uses]]

* [[New Intro Bio Approach]]

== [[Swarthmore College]]==

 
 
 
----
----
 
Please see [http://meta.wikipedia.org/wiki/MediaWiki_i18n documentation on customizing the interface]
and the [http://meta.wikipedia.org/wiki/MediaWiki_User%27s_Guide User's Guide] for usage and configuration help.
 
 
[http://www.bio.davidson.edu/GCAT GCAT Main Page]

http://parts.mit.edu/igem07/index.php/Duke/Projects/bc - bacterial communication with light.

http://parts.mit.edu/igem07/index.php/Cambridge - they talk a little about making a bacterial internet, I have no idea what they mean.

http://parts.mit.edu/igem07/index.php/Tokyo_Tech - They say, “Bistability and cell-cell communication are necessary to realize our model of ‘Balanced differentiation’.”

Main Page

2016-06-13T02:45:15Z

WikiSysop: /* Davidson Protocols */

To request GcatWiki write Access Contact [http://gcat.davidson.edu/WikiAccrss/GcatWikiAccessRequest.php Dr Malcolm Campbell]
 
==[http://gcat.davidson.edu/Gcatwiki/index.php/Davidson_Protocols Davidson Protocols]==
 

 

==[http://gcat.davidson.edu/Gcatwiki/index.php/MWSU_protocols MWSU Protocols]==
 

 
==[[Summer 2014 SynBio Project (Davidson and MWSU)]]==
 

==[[Ethics and Philosophy of SynBio]]==
 

 
==[[Summer 2013 SynBio Project (Davidson and MWSU)]]==
 
 

==[[Education Research by Caylyn Harvey]]==
 

==[[Burmese Python RNAseq Project]]==

 
==[[iRobot Energy Saver Project]]==

 
==[[Summer 2012 SynBio Project (Davidson and MWSU)]]==

 
==[[Synthetic Biology Network Research]]==

 
==[[Genome Assembly Project: Leland Taylor '12]]==
 

==[[Blueberry Genome Project for Bio343]]==

 

== [[Halomicrobium mukohataei Genome Fall 2009]] ==

 
== [[Halorhabdus utahensis Genome]] ==

 
==[[Network Research with Synthetic Biology]]==

 
== [[Missouri Western/Davidson SynBio 2011]] ==

 
== [[Missouri Western/Davidson iGEM2010]] ==

 
== [[Missouri Western/Davidson iGEM2009]] ==

 
== [[Davidson/Missouri Western iGEM2008]] ==

 
== [[MAGIC Tool Development]] ==

 
== [[Nova Southeastern University]] ==

 

== [[Davidson College]]== Small liberal arts college near Charlotte, NC.
A. Malcolm Campbell and Laurie J. Heyer GCAT faculty

* [http://gcat.davidson.edu/GcatWiki/index.php/User:Kahaynes Karmella A. Haynes], Visiting Assitant Professor of Biology

* [[A_Review_of_Synthetic_Biology |A Review of Synthetic Biology]] - Davidson College Synthetic Biology Seminar (Fall 2007)

* [[Laboratory Notebooks]]

* [[2009-2010 Biology Curriculum Wiki]]

* [[Team 5: Information Technology Initiatives]]

* [[Biological Noise and Possible Uses]]

* [[New Intro Bio Approach]]

== [[Swarthmore College]]==

 
 
 
----
----
 
Please see [http://meta.wikipedia.org/wiki/MediaWiki_i18n documentation on customizing the interface]
and the [http://meta.wikipedia.org/wiki/MediaWiki_User%27s_Guide User's Guide] for usage and configuration help.
 
 
[http://www.bio.davidson.edu/GCAT GCAT Main Page]

http://parts.mit.edu/igem07/index.php/Duke/Projects/bc - bacterial communication with light.

http://parts.mit.edu/igem07/index.php/Cambridge - they talk a little about making a bacterial internet, I have no idea what they mean.

http://parts.mit.edu/igem07/index.php/Tokyo_Tech - They say, “Bistability and cell-cell communication are necessary to realize our model of ‘Balanced differentiation’.”

Main Page

2016-06-13T02:42:45Z

WikiSysop: /* Davidson Protocols */

==[http://gcat.davidson.edu/Gcatwiki/index.php/Davidson_Protocols Davidson Protocols]==
 
To request GcatWiki write Access Contact [http://www.gcat.davidson.edu/WikiAccrss/GcatWikiAccessRequest.php Dr Malcolm Campbell]
 

==[http://gcat.davidson.edu/Gcatwiki/index.php/MWSU_protocols MWSU Protocols]==
 

 
==[[Summer 2014 SynBio Project (Davidson and MWSU)]]==
 

==[[Ethics and Philosophy of SynBio]]==
 

 
==[[Summer 2013 SynBio Project (Davidson and MWSU)]]==
 
 

==[[Education Research by Caylyn Harvey]]==
 

==[[Burmese Python RNAseq Project]]==

 
==[[iRobot Energy Saver Project]]==

 
==[[Summer 2012 SynBio Project (Davidson and MWSU)]]==

 
==[[Synthetic Biology Network Research]]==

 
==[[Genome Assembly Project: Leland Taylor '12]]==
 

==[[Blueberry Genome Project for Bio343]]==

 

== [[Halomicrobium mukohataei Genome Fall 2009]] ==

 
== [[Halorhabdus utahensis Genome]] ==

 
==[[Network Research with Synthetic Biology]]==

 
== [[Missouri Western/Davidson SynBio 2011]] ==

 
== [[Missouri Western/Davidson iGEM2010]] ==

 
== [[Missouri Western/Davidson iGEM2009]] ==

 
== [[Davidson/Missouri Western iGEM2008]] ==

 
== [[MAGIC Tool Development]] ==

 
== [[Nova Southeastern University]] ==

 

== [[Davidson College]]== Small liberal arts college near Charlotte, NC.
A. Malcolm Campbell and Laurie J. Heyer GCAT faculty

* [http://gcat.davidson.edu/GcatWiki/index.php/User:Kahaynes Karmella A. Haynes], Visiting Assitant Professor of Biology

* [[A_Review_of_Synthetic_Biology |A Review of Synthetic Biology]] - Davidson College Synthetic Biology Seminar (Fall 2007)

* [[Laboratory Notebooks]]

* [[2009-2010 Biology Curriculum Wiki]]

* [[Team 5: Information Technology Initiatives]]

* [[Biological Noise and Possible Uses]]

* [[New Intro Bio Approach]]

== [[Swarthmore College]]==

 
 
 
----
----
 
Please see [http://meta.wikipedia.org/wiki/MediaWiki_i18n documentation on customizing the interface]
and the [http://meta.wikipedia.org/wiki/MediaWiki_User%27s_Guide User's Guide] for usage and configuration help.
 
 
[http://www.bio.davidson.edu/GCAT GCAT Main Page]

http://parts.mit.edu/igem07/index.php/Duke/Projects/bc - bacterial communication with light.

http://parts.mit.edu/igem07/index.php/Cambridge - they talk a little about making a bacterial internet, I have no idea what they mean.

http://parts.mit.edu/igem07/index.php/Tokyo_Tech - They say, “Bistability and cell-cell communication are necessary to realize our model of ‘Balanced differentiation’.”

Main Page

2016-06-13T02:35:34Z

WikiSysop: /* Davidson Protocols */

==[http://gcat.davidson.edu/Gcatwiki/index.php/Davidson_Protocols Davidson Protocols]==
 
To request GcatWiki write Access Contact [http://www.gcat.davidson.edu/WikiAccrss/GcatWikiAccess.php Dr Malcolm Campbell]
 

==[http://gcat.davidson.edu/Gcatwiki/index.php/MWSU_protocols MWSU Protocols]==
 

 
==[[Summer 2014 SynBio Project (Davidson and MWSU)]]==
 

==[[Ethics and Philosophy of SynBio]]==
 

 
==[[Summer 2013 SynBio Project (Davidson and MWSU)]]==
 
 

==[[Education Research by Caylyn Harvey]]==
 

==[[Burmese Python RNAseq Project]]==

 
==[[iRobot Energy Saver Project]]==

 
==[[Summer 2012 SynBio Project (Davidson and MWSU)]]==

 
==[[Synthetic Biology Network Research]]==

 
==[[Genome Assembly Project: Leland Taylor '12]]==
 

==[[Blueberry Genome Project for Bio343]]==

 

== [[Halomicrobium mukohataei Genome Fall 2009]] ==

 
== [[Halorhabdus utahensis Genome]] ==

 
==[[Network Research with Synthetic Biology]]==

 
== [[Missouri Western/Davidson SynBio 2011]] ==

 
== [[Missouri Western/Davidson iGEM2010]] ==

 
== [[Missouri Western/Davidson iGEM2009]] ==

 
== [[Davidson/Missouri Western iGEM2008]] ==

 
== [[MAGIC Tool Development]] ==

 
== [[Nova Southeastern University]] ==

 

== [[Davidson College]]== Small liberal arts college near Charlotte, NC.
A. Malcolm Campbell and Laurie J. Heyer GCAT faculty

* [http://gcat.davidson.edu/GcatWiki/index.php/User:Kahaynes Karmella A. Haynes], Visiting Assitant Professor of Biology

* [[A_Review_of_Synthetic_Biology |A Review of Synthetic Biology]] - Davidson College Synthetic Biology Seminar (Fall 2007)

* [[Laboratory Notebooks]]

* [[2009-2010 Biology Curriculum Wiki]]

* [[Team 5: Information Technology Initiatives]]

* [[Biological Noise and Possible Uses]]

* [[New Intro Bio Approach]]

== [[Swarthmore College]]==

 
 
 
----
----
 
Please see [http://meta.wikipedia.org/wiki/MediaWiki_i18n documentation on customizing the interface]
and the [http://meta.wikipedia.org/wiki/MediaWiki_User%27s_Guide User's Guide] for usage and configuration help.
 
 
[http://www.bio.davidson.edu/GCAT GCAT Main Page]

http://parts.mit.edu/igem07/index.php/Duke/Projects/bc - bacterial communication with light.

http://parts.mit.edu/igem07/index.php/Cambridge - they talk a little about making a bacterial internet, I have no idea what they mean.

http://parts.mit.edu/igem07/index.php/Tokyo_Tech - They say, “Bistability and cell-cell communication are necessary to realize our model of ‘Balanced differentiation’.”

File:For Davidson.pptx

2016-01-12T15:18:09Z

WikiSysop:

File:Reagents RNAseq May2015.docx

2016-01-12T15:16:56Z

WikiSysop:

Main Page

2008-07-26T21:32:21Z

WikiSysop: /* Davidson College */

==This is our first attempt at GCAT Community Wiki.==
This Wiki has been set up for the use of the GCAT community. The intent is that it be maintained by its users.

Please read the [[Guidelines]] for use.

 
== [[Davidson/Missouri Western iGEM2008]] ==

 
== [[MAGIC Tool Development]] ==

 
== [[Nova Southeastern University]] ==

 

== [[Davidson College]]== Small liberal arts college near Charlotte, NC.
A. Malcolm Campbell and Laurie J. Heyer GCAT faculty

* [http://gcat.davidson.edu/GcatWiki/index.php/User:Kahaynes Karmella A. Haynes], Visiting Assitant Professor of Biology

* [[A_Review_of_Synthetic_Biology |A Review of Synthetic Biology]] - Davidson College Synthetic Biology Seminar (Fall 2007)

== [[Next School Here]]==

 
 
 
----
----
 
Please see [http://meta.wikipedia.org/wiki/MediaWiki_i18n documentation on customizing the interface]
and the [http://meta.wikipedia.org/wiki/MediaWiki_User%27s_Guide User's Guide] for usage and configuration help.
 
 
[http://www.bio.davidson.edu/GCAT GCAT Main Page]

http://parts.mit.edu/igem07/index.php/Duke/Projects/bc - bacterial communication with light.

http://parts.mit.edu/igem07/index.php/Cambridge - they talk a little about making a bacterial internet, I have no idea what they mean.

http://parts.mit.edu/igem07/index.php/Tokyo_Tech - They say, “Bistability and cell-cell communication are necessary to realize our model of ‘Balanced differentiation’.”

Davidson/Missouri Western iGEM2008

2008-04-03T15:45:19Z

WikiSysop:

<center>
Davidosn College - Missouri Western University
</center>
<center>
iGem2008
</center>

Main Page

2008-04-03T15:33:02Z

WikiSysop:

==This is our first attempt at GCAT Community Wiki.==
This Wiki has been set up for the use of the GCAT community. The intent is that it be maintained by its users.

Please read the [[Guidelines]] for use.

 
== [[Davidson/Missouri Western iGEM2008]] ==

 
== [[MAGIC Tool Development]] ==

 
== [[Nova Southeastern University]] ==

 

== [[Davidson College]]== Small liberal arts college near Charlotte, NC.
A. Malcolm Campbell and Laurie J. Heyer GCAT faculty

* [http://gcat.davidson.edu/GcatWiki/index.php/User:Kahaynes Karmella A. Haynes], Visiting Assitant Professor of Biology

* [[A_Review_of_Synthetic_Biology |A Review of Synthetic Biology - Davidson College Synthetic Biology Seminar (Fall 2007)]]

== [[Next School Here]]==

 
 
 
----
----
 
Please see [http://meta.wikipedia.org/wiki/MediaWiki_i18n documentation on customizing the interface]
and the [http://meta.wikipedia.org/wiki/MediaWiki_User%27s_Guide User's Guide] for usage and configuration help.
 
 
[http://www.bio.davidson.edu/GCAT GCAT Main Page]

CellularMemory:Main Page

2007-12-13T20:08:52Z

WikiSysop: /* Description of Wiki-Paper Contents */

{{DeLoache Top}}

=<center>Synthetic Cellular Memory</center>=

==Introduction==
Synthetic cellular memory refers to the engineering of living organisms to produce a "protracted response to a transient stimulus" ([[CellularMemory:References |Gardner, 2000; Ajo-Franklin, 2007]]). Research in this area thus far has produced simple genetic circuits that change a cell's phenotype in response to a change in environment. In the short term, construction of such gene networks provides a more thorough understanding of natural systems. By matching experimental results with mathematical models, we can put our knowledge of systems biology to the test. In the long run, cellular memory promises to be a key component of synthetic biological design. While current research efforts have been directed at the production of a reporter protein in response to some input, memory circuits hold the potential to be incorporated into more complex gene networks. Engineered cell differentiation, detection of hazardous materials in drinking water, biocomputing, gene therapy, and other such applications of synthetic devices could all one day depend on modular memory circuits similar to the ones described on this wiki site ([[CellularMemory:References |Gardner, 2000; Ajo-Franklin, 2007]]).

[[Image:Memorycartoon.png|center]]

==Description of Wiki-Paper Contents==
In order to examine the current state of the field of synthetic cellular memory, I will first look at common [[CellularMemory:Biological Designs | biological designs]] that are used to construct simple memory circuits ''in vivo''. Memory circuits typically fall into one of two categories: [[CellularMemory:Biological Designs#Mutual Repression | mutual repression]] and [[CellularMemory:Biological Designs#Autoregulatory Positive Feedback | autoregulatory positive feedback]]. Each of these networks will be described in detail. I will then present a few different [[CellularMemory:Mathematical Models | mathematical models]] that are used to describe how these biological circuits function.

From there, three different papers will be discussed in detail:

The [[CellularMemory:Toggle Switch | first paper]] describes the construction of a genetic toggle switch in ''E. coli'' ([[CellularMemory:References |Gardner, 2000]]). This was a groundbreaking paper that laid the foundation for much of the research that has since been done on synthetic gene networks. While the genetic toggle switch is one of the most simplistic forms of synthetic memory, the establishment of a predictive mathematical model and a functional biological device set the stage for more complex networks to be constructed in more complex organisms.

The [[CellularMemory:Hysteresis in Mammalian Cells | second paper]] was published five years later and discusses the construction of a [http://en.wikipedia.org/wiki/Hysteresis hysteretic] memory switch in mammalian cells ([[CellularMemory:References |Kramer, 2005]]). This circuit improves upon the bistable toggle switch by adjusting the toggle-point based on the history of the cell. This work also demonstrates the feasibility of incorporating synthetic cellular memory into eukaryotic cells.

The [[CellularMemory:Permanent Memory in Eukaryotes| final paper]], published in September of 2007, details a "permanent" memory network in yeast cells ([[CellularMemory:References |Ajo-Franklin, 2007]]). Yeast were engineered to fluoresce indefinitely after sensing an input. This system is a move towards non-rewritable synthetic cellular memory. After the cells have sensed an input, their "memory" state is retained in all environments (as opposed to toggling back and forth between two different states), even through multiple cell divisions. An accurate mathematical model was also developed to predict network behavior in this eukaryotic system based on quantitative part characterization.

After discussion of these three papers, the contents of this wiki-paper will be summarized and [[CellularMemory:Conclusions | future directions]] of the field will be analyzed.

<hr>
<center>
<Previous Section | [[CellularMemory:Biological Designs | Next Section>]]
</center>

CellularMemory:Main Page

2007-12-13T20:07:55Z

WikiSysop: /* Introduction */

{{DeLoache Top}}

=<center>Synthetic Cellular Memory</center>=

==Introduction==
Synthetic cellular memory refers to the engineering of living organisms to produce a "protracted response to a transient stimulus" ([[CellularMemory:References |Gardner, 2000; Ajo-Franklin, 2007]]). Research in this area thus far has produced simple genetic circuits that change a cell's phenotype in response to a change in environment. In the short term, construction of such gene networks provides a more thorough understanding of natural systems. By matching experimental results with mathematical models, we can put our knowledge of systems biology to the test. In the long run, cellular memory promises to be a key component of synthetic biological design. While current research efforts have been directed at the production of a reporter protein in response to some input, memory circuits hold the potential to be incorporated into more complex gene networks. Engineered cell differentiation, detection of hazardous materials in drinking water, biocomputing, gene therapy, and other such applications of synthetic devices could all one day depend on modular memory circuits similar to the ones described on this wiki site ([[CellularMemory:References |Gardner, 2000; Ajo-Franklin, 2007]]).

[[Image:Memorycartoon.png|center]]

==Description of Wiki-Paper Contents==
In order to examine the current state of the field of synthetic cellular memory, I will first look at common [[CellularMemory:Biological Designs | biological designs]] that are used to construct simple memory circuits ''in vivo''. Memory circuits typically fall into one of two categories: [[CellularMemory:Biological Designs#Mutual Repression | mutual repression]] and [[CellularMemory:Biological Designs#Autoregulatory Positive Feedback | autoregulatory positive feedback]]. Each of these networks will be described in detail. I will then present a few different [[CellularMemory:Mathematical Models | mathematical models]] that are used to describe how these biological circuits function.

From there, three different papers will be discussed in detail:

The [[CellularMemory:Toggle Switch | first paper]] describes the construction of a genetic toggle switch in ''E. coli'' ([[CellularMemory:References |Gardner, 2000]]). Published in 2000, this was a groundbreaking paper that laid the foundation for much of the research that has since been done on synthetic gene networks. While the genetic toggle switch is one of the most simplistic forms of synthetic memory, the establishment of a predictive mathematical model and a functional biological device set the stage for more complex networks to be constructed in more complex organisms.

The [[CellularMemory:Hysteresis in Mammalian Cells | second paper]] was published five years later and discusses the construction of a [http://en.wikipedia.org/wiki/Hysteresis hysteretic] memory switch in mammalian cells ([[CellularMemory:References |Kramer, 2005]]). This circuit improves upon the bistable toggle switch by adjusting the toggle-point based on the history of the cell. This work also demonstrates the feasibility of incorporating synthetic cellular memory into eukaryotic cells.

The [[CellularMemory:Permanent Memory in Eukaryotes| final paper]], published in September of 2007, details a "permanent" memory network in yeast cells ([[CellularMemory:References |Ajo-Franklin, 2007]]). Yeast were engineered to fluoresce indefinitely after sensing an input. This system is a move towards non-rewritable synthetic cellular memory. After the cells have sensed an input, their "memory" state is retained in all environments (as opposed to toggling back and forth between two different states), even through multiple cell divisions. An accurate mathematical model was also developed to predict network behavior in this eukaryotic system based on quantitative part characterization.

After discussion of these three papers, the contents of this wiki-paper will be summarized and [[CellularMemory:Conclusions | future directions]] of the field will be analyzed.

<hr>
<center>
<Previous Section | [[CellularMemory:Biological Designs | Next Section>]]
</center>

CellularMemory:Main Page

2007-12-13T20:06:47Z

WikiSysop: /* Introduction */

{{DeLoache Top}}

=<center>Synthetic Cellular Memory</center>=

==Introduction==
Synthetic cellular memory refers to the engineering of living organisms to produce a "protracted response to a transient stimulus" ([[CellularMemory:References |Gardner, 2000; Ajo-Franklin, 2007]]). Research in this area thus far has produced simple genetic circuits that change a cell's phenotype in response to a change in environment. In the short term, construction of such gene networks provides a more thorough understanding of natural systems. By matching experimental results with mathematical models, we can put our knowledge of systems biology to the test. In the long run, cellular memory promises to be a key component of synthetic biological design. While current research efforts have been directed at the production of a reporter protein in response to some input, memory circuits hold the potential to be incorporated into more complex gene networks. Engineered cell differentiation, detection of hazardous materials in drinking water, biocomputing, gene therapy, and other such applications of synthetic devices could all one day depend on modular memory circuits similar to the ones described in this paper ([[CellularMemory:References |Gardner, 2000 and Ajo-Franklin, 2007]]).

[[Image:Memorycartoon.png|center]]

==Description of Wiki-Paper Contents==
In order to examine the current state of the field of synthetic cellular memory, I will first look at common [[CellularMemory:Biological Designs | biological designs]] that are used to construct simple memory circuits ''in vivo''. Memory circuits typically fall into one of two categories: [[CellularMemory:Biological Designs#Mutual Repression | mutual repression]] and [[CellularMemory:Biological Designs#Autoregulatory Positive Feedback | autoregulatory positive feedback]]. Each of these networks will be described in detail. I will then present a few different [[CellularMemory:Mathematical Models | mathematical models]] that are used to describe how these biological circuits function.

From there, three different papers will be discussed in detail:

The [[CellularMemory:Toggle Switch | first paper]] describes the construction of a genetic toggle switch in ''E. coli'' ([[CellularMemory:References |Gardner, 2000]]). Published in 2000, this was a groundbreaking paper that laid the foundation for much of the research that has since been done on synthetic gene networks. While the genetic toggle switch is one of the most simplistic forms of synthetic memory, the establishment of a predictive mathematical model and a functional biological device set the stage for more complex networks to be constructed in more complex organisms.

The [[CellularMemory:Hysteresis in Mammalian Cells | second paper]] was published five years later and discusses the construction of a [http://en.wikipedia.org/wiki/Hysteresis hysteretic] memory switch in mammalian cells ([[CellularMemory:References |Kramer, 2005]]). This circuit improves upon the bistable toggle switch by adjusting the toggle-point based on the history of the cell. This work also demonstrates the feasibility of incorporating synthetic cellular memory into eukaryotic cells.

The [[CellularMemory:Permanent Memory in Eukaryotes| final paper]], published in September of 2007, details a "permanent" memory network in yeast cells ([[CellularMemory:References |Ajo-Franklin, 2007]]). Yeast were engineered to fluoresce indefinitely after sensing an input. This system is a move towards non-rewritable synthetic cellular memory. After the cells have sensed an input, their "memory" state is retained in all environments (as opposed to toggling back and forth between two different states), even through multiple cell divisions. An accurate mathematical model was also developed to predict network behavior in this eukaryotic system based on quantitative part characterization.

After discussion of these three papers, the contents of this wiki-paper will be summarized and [[CellularMemory:Conclusions | future directions]] of the field will be analyzed.

<hr>
<center>
<Previous Section | [[CellularMemory:Biological Designs | Next Section>]]
</center>

Medical Applications of Synthetic Biology - Samantha Simpson

2007-12-13T19:36:23Z

WikiSysop: /* Paper */

== Project Proposal ==

My project will be on medical applications of synthetic biology. I will reference Anderson's paper on utilizing quorum-sensing and hypoxia-responsive genes coupled with invasin from ''Yersinia tuberculosis'' to invade cancer-causing cells. Coupled with Critchley's work, which describes a bacterial system that invades eukaryotic cells and delivers proteins coded for in the bacteria's genome, one could possibly create 'search and destroy' ''E.coli'' that can locate, invade, and kill tumor cells. Garmory's paper also highlights the possibility of using bacteria as a drug-delivering system, specifically vaccine vectors. Kobayashi describes cells that produce a protective biofilm layer after exposure to DNA-damaging agents. Ro engineers yeast to create an anti-malarial drug that can be created at a lower cost than the previous standard production process. These papers, and others that have a focus on direct medical applications such as novel cancer therapies, vaccination technology, biological protection, and the creation of new medicines, will be referenced to create a cohesive overview of applications of synthetic biology to the medical field.

== Paper ==

Synthetic biology is a rapidly emerging field that strives to re-design existing biological systems and components or fabricate novel biological systems and components. The end result of experimentation in synthetic biology is to help scientists understand a naturally occurring genetic pathway more fully or to create a new, useful pathway. Recent research efforts have focused on engineering bacteria to create a new form of biofuel or to make promoters of varying efficiencies for more exact gene expression. Other research efforts focus on engineering yeast to make antimalarial drugs or engineering ''E. coli'' to recognize and invade tumor-like cells, both of which are medical applications of synthetic biology. Advancements such as novel cancer therapies, vaccination technology, biological protection, and the creation of new medicines highlight synthetic biology’s potential to create breakthroughs in both the prevention and treatment side of medical science. This paper will examine various medical applications of synthetic biology and further development that needs to be done before current research can be used in hospitals and doctors’ offices.

'''Prevention'''

Engineering live bacteria to act as a vaccine vector is an area of interest in synthetic biology. Bacteria were first thought to be good vectors because they would not degrade at mucosal surface and would survive the low pH in the gastrointestinal tract like most other orally administered antigens (Garmory, 2003). A particular strain of ''Salmonella'' with one or more deletions in the [http://www.ncbi.nlm.nih.gov/sites/entrez?db=pubmed&uid=15012217&cmd=showdetailview&indexed=google shikimate pathway], which creates a precursor for phenylalanine and tyrosine, is a promising vector because it gets into the body easily but does not harmfully effect the tissue and is gone within a week or two of introduction. Scientists engineered this strain to carry the [http://en.wikipedia.org/wiki/Yersinia_pestis ''Yersinia pestis''] V antigen, and studied its ability to protect mice from ''Y. pestis''. The V antigen is generally injected via an intramuscular route, however, in this study, 20 mice were given the V antigen via the intragastric route so scientists could test the effectiveness of an oral administration. Unfortunately, the inoculation was not extremely successful and only 6 of 20 mice infected with ''Y. pestis'' showed a strong response (Garmory, 2003). Garmory and colleagues cited several reasons for this: the copy number of the plasmid containing the V antigen might not have been high enough; the attenuated ''Salmonella'' may not have been able to reach cells that would be targeted by ''Y. pestis''; and the inability of the cell to secrete the antigen. The authors remain hopeful that attenuated bacteria vectors may someday produce an orally administered long-lasting response for protection against bubonic and pneumonic plague (Garmory, 2003). Many changes need to be made to live vector vaccine design before it is an effective method of treating or preventing disease, such as solving the issue of specifity of a vaccine administered orally, and creating a bacterial chassey that can secrete an antigen at an appropriate time.

Another method of disease prevention with a synthetic biology approach is related to skin cancer. ''E. coli'' was engineered to sense single-stranded DNA, and this DNA damage was coupled with a mechanism to produce a biofilm around the cell. The biofilm output module was tested with DNA damage done by [http://en.wikipedia.org/wiki/Mitomycin_c mitomycin C], a DNA crosslinking agent, or UV irradiation, a known cause of skin cancer. Single-stranded DNA activates [http://en.wikipedia.org/wiki/RecA RecA], which in turn represses the C1 repressor protein, thus allowing the transcription of the PL promoter (Kobayashi, 2004). The PL promoter is on the biofilm-forming output plasmid (pBFR), which controls biofilm formation (Fig. 1). The biofilm protection, when part of a toggle switch mechanism, lasted indefinitely when pulsed with UV light at 8 J/m2 for only 2 seconds (Kobayashi, 2004) (Fig. 2). The biofilm-production mechanism has not been engineered in eukaryotic cells, but a prokaryote-based system that could aid with human skin disease is imaginable. For example, a sunscreen that changes colors when the DNA damage may be too intense for a sunbather as a warning, or a sunscreen that becomes more protective as the possibility of genetic damage increased. Further research should focus on how to best implement the biofilm producing output module to protect human DNA.

<center>

{|
|-
|[[Image:Toggleswitch.JPG]]
|[[Image:UVinducedbiofilm.JPG]]
|-
|'''Fig. 1.''' The RecA / traA toggleswitch mechanism. (Kobayashi, 2004 - Permission Pending)
|'''Fig. 2.''' Crystal violet absorbance measures biofilm formation. (Kobayashi, 2004 - Permission Pending)
|}
</center>

'''Treatment'''

Synthetic biologists consider finding a cure for cancer a reachable goal. Previous to the development of synthetic biology as a field, it was known that three types of bacteria, ''Bifodobacterium'', ''Clostridium'', and ''Salmonella'' all preferentially infect the dense cells of tumors (Pawelek, 2003). All three types of bacteria are associated with reducing tumor size in patients who are infected with them, and it was noted that all three bacteria act anaerobically. Capitalizing on the idea that tumors create an unusually dense, anaerobic mass of cells, Christopher Voigt’s lab engineered bacteria to identify and invade cancer cells. They utilized the ''lux'' quorum sensing from ''Vibrio fischeri'' to identify cells growing at high densities (Fig. 3) and the ''fdhF'' promoter to identify cells growing in anaerobic conditions ''in vitro'' (Fig. 4) (Anderson, 2006). They coupled this identification mechanism with the invasin output module from ''Yersinia pseudotuberculosis'' so the ''E. coli'' could invade specific cells.

<center>

{|
|-
|[[Image:InvasinDensity.JPG]]
|[[Image:InvasinAnaerobic2.JPG]]
|-
|'''Fig. 3.''' The red line represents when the invasin output module was paired with the inducible promoter ''lux'' which senses high cell density, the black line is constitutively on invasin. (Anderson, 2006 - Permission Pending)
|'''Fig. 4.''' The invasin output module was paired with the inducible promoter ''fdhF'' which senses anaerobic conditions. (Anderson, 2006 - Permission Pending)
|}

</center>

Next, scientists hope to insert a gene that would destroy the invaded cells, making the complete cycle a search, invade, and destroy loop that would only be sensitive to cancer cells. One promising destroy mechanism has been implemented in humans via intratumoral injection, based on ''Salmonella'' that contains the ''E. coli'' cytosine deaminase gene that converts 5-fluorocytosine to 5-fluorouracil, a chemotherapy drug (Nemunaitis, 2003). Unfortunately, while the tumors did produce [http://en.wikipedia.org/wiki/Fluorouracil 5-fluorouracil] (5-FU), they did not produce it in high enough yields to cause regression of the cancer. Another experiment flooded mouse cells via intratumoral injection with 6-methylpurine-2’deoxyribose (6-MPDR), which was converted to the toxin [http://jpet.aspetjournals.org/cgi/content/abstract/304/3/1280 6-methyl purine] (MeP) by the enzyme purine nucleoside phosphorylase (PNP), which is naturally found in ''E. coli'' (Critchley, 2004) (Fig. 5).

<center>

[[Image:Tumor.JPG]]
</center>

'''Fig. 5.''' Tumor size after injections. Cancer cells were injected into mice, and 5 days later injections with PBS, 6-MPDR, and invasin-enhanced ''E-coli'' began. Tumor cells recieving injections of invasin-enhanced ''E.coli'' and 6-MPDR grew the slowest, but did not get smaller. (Critchley, 2004 - Permission Pending)

The problem with this is that even though MeP was injected into the tumor, all cells in the body might be exposed to the toxin. In addition to the search and destroy mechanism, scientists hope to increase selectivity by combining the hypoxia-sensing and density-sensing units so that ''E. coli'' will only enter eukaryotic cells that exhibit both characteristics. This would make it more likely that invasin-enhanced ''E. coli'' would only invade cancer cells rather than muscle tissue that had low oxygen due to exercise (Anderson, 2006). To create a fully functional, synthetic biology-based approach to cancer therapy, scientists need to develop bacteria that discriminately invades cancer cells and destroys them. Most of the pieces are there – scientists have engineered ''E. coli'' that selectively invades hypoxic cells or dense cells and ''Salmonella'' that can destroy tumors in vivo – they just need to be put together effectively.

Finally, synthetic biology has made its mark on the field of medicine by creating drugs that are less expensive to produce than those made by pharmaceutical companies. Jay Keasling’s project involving the antimalarial drug artemisinin is the best example. Yeast was engineered to increase normal farnesyl pyrophosphate (FPP) production, convert FPP to amorphadiene, and oxidizing amorphadiene to artemisinic acid, which can then easily be separated from the yeast cells and converted to artemisinin in the laboratory (Fig. 6). This innovation allows the production of artemisinin at lower prices than those currently on the market, and without regard to environmental constraints (Ro, 2006). On a completely different tangent, Collins’s lab engineered T7 bacteriophages to deliver biofilm-degrading enzymes such as dispersin B after infecting and replicating in an ''E. coli'' biofilm. Dispersin B works to degrade an adhesin that is critical for biofilm formation. Phages that released dispersin B were found to be 99.997% effective at removing biofilm, which is 4.5 orders of magnitude higher than non-enzymatic phages, and caused a 3.65 log10 reduction in bacterial cells recovered from biofilmwhen compared to untreated biofilms (Lu, 2007) (Fig. 7). The specificity of phages for a certain type of bacteria make them a viable vector to be used in human treatments, which may range from removing dental plaques to cleaning inserted medical devices. Before phages are ready for use in medicine, however, a well-characterized phage library must be created so that way any specific biofilm can be targeted using the dispersin B method. This also requires scientists being able to identify the type of bacteria in the biofilm, which may be growing inside the body. Using synthetic biology to engineer an antimalarial drug in yeast or a biofilm-destroying enzyme in bacteriophages greatly increases the possible contributions to the field of medicine.

<center>

{|
|-
|[[Image:Artemesinin.JPG]]
|[[Image:BiofilmDegradation.JPG]]
|-
|'''Fig. 6.''' All the steps in the box indicate steps that were optimized in yeast for a maximum artemisinic acid output. (Ro, 2006 - Permission Pending)
|'''Fig. 7.''' The amount of biofilm was decreased when the dispersin B enzymes were incorporated into T7 bacteriophages. (Lu, 2007 - Permission Pending)
|}
</center>

'''Conclusion'''

Medical applications of synthetic biology are wide-ranging and eminently applicable to daily life. Synthetic biology based cancer treatments that utilize ''E.coli'' engineered to sense and attack tissues with high cell density and low oxygen availability are promising and need to be tested in vivo. Designing vectors out of attenuated bacteria may be a safe, effective method for disease treatment, but need to be able to reach a target tissue if administered orally. Using biofilms to sense sun damage potential may decrease the likelihood of skin cancer. Finally, using engineered cells to produce medicines in a cost-effective, environmentally-friendly way may revolutionize the pharmaceutical industry. Investing more research efforts in the field of synthetic biology may turn out to be an investment in medical technology.

== Works Cited ==

-[http://www.bio.davidson.edu/courses/synthetic/papers/2006_anderson.pdf Anderson JC, Clarke EJ, Arkin, AP, and Voigt CA (2006). Environmentally controlled invasion of cancer cells by engineered bacteria. Journal of Molecular Biology 355:619-27.]

-[http://www.nature.com/gt/journal/v11/n15/abs/3302281a.html Critchley RJ, Jezzard S, Radford KJ, Goussard S, Lemoine NR, et al. (2004). Potential therapeutic applications of recombinant, invasive ''E. coli''. Gene Therapy 11:1224-33.]

-[http://www.ncbi.nlm.nih.gov/sites/entrez?db=pubmed&uid=15203915&cmd=showdetailview&indexed=google Garmory HS, Leary SEC, Griffin, KF, Williamson D, Brown, KA, and Titball RW (2003). The use of live attenuated bacteria as a delivery system for heterologous antigens. Journal of Drug Targeting 11:471-79.]

-[http://www.pnas.org/cgi/reprint/101/22/8414 Kobayashi H, et al. (2004). Programmable cells: Interfacing natural and engineered gene networks. PNAS 101: 8414-19.]

-[http://www.bio.davidson.edu/courses/synthetic/papers/Phage_BioFilms.pdf Lu, TK, and Collins JJ (2007). Dispersing biofilms with engineered enzymatic bacteriophage. PNAS 104: 11197-11202.]

-[http://www.nature.com/cgt/journal/v10/n10/abs/7700634a.html Nemunaitis J, Cunningham C, Senzer N, Kuhn J, Cramm J, Litz C, et al. (2003). Pilot trial of genetically modified, attenuated ''Salmonella'' expressing cytosine deaminase gene in refractory cancer patients. Cancer Gene Therapy 10:737-744.]

-[http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6W85-49FGKHC-N&_user=10&_coverDate=09%2F30%2F2003&_rdoc=1&_fmt=&_orig=search&_sort=d&view=c&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=d4529b45974bfe7d9ddab1fc865f088b Pawelek JM, Low KB, and Bermudes D (2003). Bacteria as tumour-targeting vectors. Lancet Oncology 4:548-56.]

-[http://www.bio.davidson.edu/courses/synthetic/papers/Keasling_malaria.pdf Ro D, et al. (2006). Production of the antimalarial drug precursor artemisinic acid in engineered yeast. Nature 440:940-43.]

Medical Applications of Synthetic Biology - Samantha Simpson

2007-12-13T19:32:07Z

WikiSysop: /* Paper */

== Project Proposal ==

My project will be on medical applications of synthetic biology. I will reference Anderson's paper on utilizing quorum-sensing and hypoxia-responsive genes coupled with invasin from ''Yersinia tuberculosis'' to invade cancer-causing cells. Coupled with Critchley's work, which describes a bacterial system that invades eukaryotic cells and delivers proteins coded for in the bacteria's genome, one could possibly create 'search and destroy' ''E.coli'' that can locate, invade, and kill tumor cells. Garmory's paper also highlights the possibility of using bacteria as a drug-delivering system, specifically vaccine vectors. Kobayashi describes cells that produce a protective biofilm layer after exposure to DNA-damaging agents. Ro engineers yeast to create an anti-malarial drug that can be created at a lower cost than the previous standard production process. These papers, and others that have a focus on direct medical applications such as novel cancer therapies, vaccination technology, biological protection, and the creation of new medicines, will be referenced to create a cohesive overview of applications of synthetic biology to the medical field.

== Paper ==

Synthetic biology is a rapidly emerging field that strives to re-design existing biological systems and components or fabricate novel biological systems and components. The end result of experimentation in synthetic biology is to help scientists understand a naturally occurring genetic pathway more fully or to create a new, useful pathway. Recent research efforts have focused on engineering bacteria to create a new form of biofuel or to make promoters of varying efficiencies for more exact gene expression. Other research efforts focus on engineering yeast to make antimalarial drugs or engineering ''E. coli'' to recognize and invade tumor-like cells, both of which are medical applications of synthetic biology. Advancements such as novel cancer therapies, vaccination technology, biological protection, and the creation of new medicines highlight synthetic biology’s potential to create breakthroughs in both the prevention and treatment side of medical science. This paper will examine various medical applications of synthetic biology and further development that needs to be done before current research can be used in hospitals and doctors’ offices.

'''Prevention'''

Engineering live bacteria to act as a vaccine vector is an area of interest in synthetic biology. Bacteria were first thought to be good vectors because they would not degrade at mucosal surface and would survive the low pH in the gastrointestinal tract like most other orally administered antigens (Garmory, 2003). A particular strain of ''Salmonella'' with one or more deletions in the [http://www.ncbi.nlm.nih.gov/sites/entrez?db=pubmed&uid=15012217&cmd=showdetailview&indexed=google shikimate pathway], which creates a precursor for phenylalanine and tyrosine, is a promising vector because it gets into the body easily but does not harmfully effect the tissue and is gone within a week or two of introduction. Scientists engineered this strain to carry the [http://en.wikipedia.org/wiki/Yersinia_pestis ''Yersinia pestis''] V antigen, and studied its ability to protect mice from ''Y. pestis''. The V antigen is generally injected via an intramuscular route, however, in this study, 20 mice were given the V antigen via the intragastric route so scientists could test the effectiveness of an oral administration. Unfortunately, the inoculation was not extremely successful and only 6 of 20 mice infected with ''Y. pestis'' showed a strong response (Garmory, 2003). Garmory and colleagues cited several reasons for this: the copy number of the plasmid containing the V antigen might not have been high enough; the attenuated ''Salmonella'' may not have been able to reach cells that would be targeted by ''Y. pestis''; and the inability of the cell to secrete the antigen. The authors remain hopeful that attenuated bacteria vectors may someday produce an orally administered long-lasting response for protection against bubonic and pneumonic plague (Garmory, 2003). Many changes need to be made to live vector vaccine design before it is an effective method of treating or preventing disease, such as solving the issue of specifity of a vaccine administered orally, and creating a bacterial chassey that can secrete an antigen at an appropriate time.

Another method of disease prevention with a synthetic biology approach is related to skin cancer. ''E. coli'' was engineered to sense single-stranded DNA, and this DNA damage was coupled with a mechanism to produce a biofilm around the cell. The biofilm output module was tested with DNA damage done by [http://en.wikipedia.org/wiki/Mitomycin_c mitomycin C], a DNA crosslinking agent, or UV irradiation, a known cause of skin cancer. Single-stranded DNA activates [http://en.wikipedia.org/wiki/RecA RecA], which in turn represses the C1 repressor protein, thus allowing the transcription of the PL promoter (Kobayashi, 2004). The PL promoter is on the biofilm-forming output plasmid (pBFR), which controls biofilm formation (Fig. 1). The biofilm protection, when part of a toggle switch mechanism, lasted indefinitely when pulsed with UV light at 8 J/m2 for only 2 seconds (Kobayashi, 2004) (Fig. 2). The biofilm-production mechanism has not been engineered in eukaryotic cells, but a prokaryote-based system that could aid with human skin disease is imaginable. For example, a sunscreen that changes colors when the DNA damage may be too intense for a sunbather as a warning, or a sunscreen that becomes more protective as the possibility of genetic damage increased. Further research should focus on how to best implement the biofilm producing output module to protect human DNA.

<center>

{|
|-
|[[Image:Toggleswitch.JPG]]
|[[Image:UVinducedbiofilm.JPG]]
|-
|'''Fig. 1.''' The RecA / traA toggleswitch mechanism. (Kobayashi, 2004 - Permission Pending)
|'''Fig. 2.''' Crystal violet absorbance measures biofilm formation. (Kobayashi, 2004 - Permission Pending)
|}
</center>

'''Treatment'''

Synthetic biologists consider finding a cure for cancer a reachable goal. Previous to the development of synthetic biology as a field, it was known that three types of bacteria, ''Bifodobacterium'', ''Clostridium'', and ''Salmonella'' all preferentially infect the dense cells of tumors (Pawelek, 2003). All three types of bacteria are associated with reducing tumor size in patients who are infected with them, and it was noted that all three bacteria act anaerobically. Capitalizing on the idea that tumors create an unusually dense, anaerobic mass of cells, Christopher Voigt’s lab engineered bacteria to identify and invade cancer cells. They utilized the ''lux'' quorum sensing from ''Vibrio fischeri'' to identify cells growing at high densities (Fig. 3) and the ''fdhF'' promoter to identify cells growing in anaerobic conditions ''in vitro'' (Fig. 4) (Anderson, 2006). They coupled this identification mechanism with the invasin output module from ''Yersinia pseudotuberculosis'' so the ''E. coli'' could invade specific cells.

<center>

{|
|-
|[[Image:InvasinDensity.JPG]]
|[[Image:InvasinAnaerobic2.JPG]]
|-
|'''Fig. 3.''' The red line represents when the invasin output module was paired with the inducible promoter ''lux'' which senses high cell density, the black line is constitutively on invasin. (Anderson, 2006 - Permission Pending)
|'''Fig. 4.''' The invasin output module was paired with the inducible promoter ''fdhF'' which senses anaerobic conditions. (Anderson, 2006 - Permission Pending)
|}

</center>

Next, scientists hope to insert a gene that would destroy the invaded cells, making the complete cycle a search, invade, and destroy loop that would only be sensitive to cancer cells. One promising destroy mechanism has been implemented in humans via intratumoral injection, based on ''Salmonella'' that contains the ''E. coli'' cytosine deaminase gene that converts 5-fluorocytosine to 5-fluorouracil, a chemotherapy drug (Nemunaitis, 2003). Unfortunately, while the tumors did produce [http://en.wikipedia.org/wiki/Fluorouracil 5-fluorouracil] (5-FU), they did not produce it in high enough yields to cause regression of the cancer. Another experiment flooded mouse cells via intratumoral injection with 6-methylpurine-2’deoxyribose (6-MPDR), which was converted to the toxin [http://jpet.aspetjournals.org/cgi/content/abstract/304/3/1280 6-methyl purine] (MeP) by the enzyme purine nucleoside phosphorylase (PNP), which is naturally found in ''E. coli'' (Critchley, 2004) (Fig. 5).

<center>

[[Image:Tumor.JPG]]
</center>

'''Fig. 5.''' Tumor size after injections. Cancer cells were injected into mice, and 5 days later injections with PBS, 6-MPDR, and invasin-enhanced ''E-coli'' began. Tumor cells recieving injections of invasin-enhanced ''E.coli'' and 6-MPDR grew the slowest, but did not get smaller. (Critchley, 2004 - Permission Pending)

The problem with this is that even though MeP was injected into the tumor, all cells in the body might be exposed to the toxin. In addition to the search and destroy mechanism, scientists hope to increase selectivity by combining the hypoxia-sensing and density-sensing units so that ''E. coli'' will only enter eukaryotic cells that exhibit both characteristics. This would make it more likely that invasin-enhanced ''E. coli'' would only invade cancer cells rather than muscle tissue that had low oxygen due to exercise (Anderson, 2006). To create a fully functional, synthetic biology-based approach to cancer therapy, scientists need to develop bacteria that discriminately invades cancer cells and destroys them. Most of the pieces are there – scientists have engineered ''E. coli'' that selectively invades hypoxic cells or dense cells and ''Salmonella'' that can destroy tumors in vivo – they just need to be put together effectively.

Finally, synthetic biology has made its mark on the field of medicine by creating drugs that are less expensive to produce than those made by pharmaceutical companies. Jay Keasling’s project involving the antimalarial drug artemisinin is the best example. Yeast was engineered to increase normal farnesyl pyrophosphate (FPP) production, convert FPP to amorphadiene, and oxidizing amorphadiene to artemisinic acid, which can then easily be separated from the yeast cells and converted to artemisinin in the laboratory (Fig. 6). This innovation allows the production of artemisinin at lower prices than those currently on the market, and without regard to environmental constraints (Ro, 2006). On a completely different tangent, Collins’s lab engineered T7 bacteriophages to deliver biofilm-degrading enzymes such as dispersin B after infecting and replicating in an ''E. coli'' biofilm. Dispersin B works to degrade an adhesin that is critical for biofilm formation. Phages that released dispersin B were found to be 99.997% effective at removing biofilm, which is 4.5 orders of magnitude higher than non-enzymatic phages, and caused a 3.65log10 reduction in bacterial cells recovered from biofilmwhen compared to untreated biofilms (Lu, 2007) (Fig. 7). The specificity of phages for a certain type of bacteria make them a viable vector to be used in human treatments, which may range from removing dental plaques to cleaning inserted medical devices. Before phages are ready for use in medicine, however, a well-characterized phage library must be created so that way any specific biofilm can be targeted using the dispersin B method. This also requires scientists being able to identify the type of bacteria in the biofilm, which may be growing inside the body. Using synthetic biology to engineer an antimalarial drug in yeast or a biofilm-destroying enzyme in bacteriophages greatly increases the possible contributions to the field of medicine.

<center>

{|
|-
|[[Image:Artemesinin.JPG]]
|[[Image:BiofilmDegradation.JPG]]
|-
|'''Fig. 6.''' All the steps in the box indicate steps that were optimized in yeast for a maximum artemisinic acid output. (Ro, 2006 - Permission Pending)
|'''Fig. 7.''' The amount of biofilm was decreased when the dispersin B enzymes were incorporated into T7 bacteriophages. (Lu, 2007 - Permission Pending)
|}
</center>

'''Conclusion'''

Medical applications of synthetic biology are wide-ranging and eminently applicable to daily life. Synthetic biology based cancer treatments that utilize ''E.coli'' engineered to sense and attack tissues with high cell density and low oxygen availability are promising and need to be tested in vivo. Designing vectors out of attenuated bacteria may be a safe, effective method for disease treatment, but need to be able to reach a target tissue if administered orally. Using biofilms to sense sun damage potential may decrease the likelihood of skin cancer. Finally, using engineered cells to produce medicines in a cost-effective, environmentally-friendly way may revolutionize the pharmaceutical industry. Investing more research efforts in the field of synthetic biology may turn out to be an investment in medical technology.

== Works Cited ==

-[http://www.bio.davidson.edu/courses/synthetic/papers/2006_anderson.pdf Anderson JC, Clarke EJ, Arkin, AP, and Voigt CA (2006). Environmentally controlled invasion of cancer cells by engineered bacteria. Journal of Molecular Biology 355:619-27.]

-[http://www.nature.com/gt/journal/v11/n15/abs/3302281a.html Critchley RJ, Jezzard S, Radford KJ, Goussard S, Lemoine NR, et al. (2004). Potential therapeutic applications of recombinant, invasive ''E. coli''. Gene Therapy 11:1224-33.]

-[http://www.ncbi.nlm.nih.gov/sites/entrez?db=pubmed&uid=15203915&cmd=showdetailview&indexed=google Garmory HS, Leary SEC, Griffin, KF, Williamson D, Brown, KA, and Titball RW (2003). The use of live attenuated bacteria as a delivery system for heterologous antigens. Journal of Drug Targeting 11:471-79.]

-[http://www.pnas.org/cgi/reprint/101/22/8414 Kobayashi H, et al. (2004). Programmable cells: Interfacing natural and engineered gene networks. PNAS 101: 8414-19.]

-[http://www.bio.davidson.edu/courses/synthetic/papers/Phage_BioFilms.pdf Lu, TK, and Collins JJ (2007). Dispersing biofilms with engineered enzymatic bacteriophage. PNAS 104: 11197-11202.]

-[http://www.nature.com/cgt/journal/v10/n10/abs/7700634a.html Nemunaitis J, Cunningham C, Senzer N, Kuhn J, Cramm J, Litz C, et al. (2003). Pilot trial of genetically modified, attenuated ''Salmonella'' expressing cytosine deaminase gene in refractory cancer patients. Cancer Gene Therapy 10:737-744.]

-[http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6W85-49FGKHC-N&_user=10&_coverDate=09%2F30%2F2003&_rdoc=1&_fmt=&_orig=search&_sort=d&view=c&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=d4529b45974bfe7d9ddab1fc865f088b Pawelek JM, Low KB, and Bermudes D (2003). Bacteria as tumour-targeting vectors. Lancet Oncology 4:548-56.]

-[http://www.bio.davidson.edu/courses/synthetic/papers/Keasling_malaria.pdf Ro D, et al. (2006). Production of the antimalarial drug precursor artemisinic acid in engineered yeast. Nature 440:940-43.]

Medical Applications of Synthetic Biology - Samantha Simpson

2007-12-13T15:01:21Z

WikiSysop: /* Paper */

== Project Proposal ==

My project will be on medical applications of synthetic biology. I will reference Anderson's paper on utilizing quorum-sensing and hypoxia-responsive genes coupled with invasin from ''Yersinia tuberculosis'' to invade cancer-causing cells. Coupled with Critchley's work, which describes a bacterial system that invades eukaryotic cells and delivers proteins coded for in the bacteria's genome, one could possibly create 'search and destroy' ''E.coli'' that can locate, invade, and kill tumor cells. Garmory's paper also highlights the possibility of using bacteria as a drug-delivering system, specifically vaccine vectors. Kobayashi describes cells that produce a protective biofilm layer after exposure to DNA-damaging agents. Ro engineers yeast to create an anti-malarial drug that can be created at a lower cost than the previous standard production process. These papers, and others that have a focus on direct medical applications such as novel cancer therapies, vaccination technology, biological protection, and the creation of new medicines, will be referenced to create a cohesive overview of applications of synthetic biology to the medical field.

== Paper ==

Synthetic biology is a rapidly emerging field that strives to re-design existing biological systems and components or fabricate novel biological systems and components. The end result of experimentation in synthetic biology is to help scientists understand a naturally occurring genetic pathway more fully or to create a new, useful pathway. Recent research efforts have focused on engineering bacteria to create a new form of biofuel or to make promoters of varying efficiencies for more exact gene expression. Other research efforts focus on engineering yeast to make antimalarial drugs or engineering ''E. coli'' to recognize and invade tumor-like cells, both of which are medical applications of synthetic biology. Advancements such as novel cancer therapies, vaccination technology, biological protection, and the creation of new medicines highlight synthetic biology’s potential to create breakthroughs in both the prevention and treatment side of medical science. This paper will examine various medical applications of synthetic biology and further development that needs to be done before current research can be used in hospitals and doctors’ offices.

'''Prevention'''

Engineering live bacteria to act as a vaccine vector is an area of interest in synthetic biology. Bacteria were first thought to be good vectors because they would not degrade at mucosal surface and would survive the low pH in the gastrointestinal tract like most other orally administered antigens (Garmory, 2003). A particular strain of ''Salmonella'' with one or more deletions in the [http://www.ncbi.nlm.nih.gov/sites/entrez?db=pubmed&uid=15012217&cmd=showdetailview&indexed=google shikimate pathway], which creates a precursor for phenylalanine and tyrosine, is a promising vector because it gets into the body easily but does not harmfully effect the tissue and is gone within a week or two of introduction. Scientists engineered this strain to carry the [http://en.wikipedia.org/wiki/Yersinia_pestis ''Yersinia pestis''] V antigen, and studied its ability to protect mice from ''Y. pestis''. The V antigen is generally injected via an intramuscular route, however, in this study, 20 mice were given the V antigen via the intragastric route so scientists could test the effectiveness of an oral administration. Unfortunately, the inoculation was not extremely successful and only 6 of 20 mice infected with ''Y. pestis'' showed a strong response (Garmory, 2003). Garmory and colleagues cited several reasons for this: the copy number of the plasmid containing the V antigen might not have been high enough; the attenuated ''Salmonella'' may not have been able to reach cells that would be targeted by ''Y. pestis''; and the inability of the cell to secrete the antigen. The authors remain hopeful that attenuated bacteria vectors may someday produce an orally administered long-lasting response for protection against bubonic and pneumonic plague (Garmory, 2003). Many changes need to be made to live vector vaccine design before it is an effective method of treating or preventing disease, such as solving the issue of specifity of a vaccine administered orally, and creating a bacterial chassey that can secrete an antigen at an appropriate time.

Another method of disease prevention with a synthetic biology approach is related to skin cancer. ''E. coli'' was engineered to sense single-stranded DNA, and this DNA damage was coupled with a mechanism to produce a biofilm around the cell. The biofilm output module was tested with DNA damage done by [http://en.wikipedia.org/wiki/Mitomycin_c mitomycin C], a DNA crosslinking agent, or UV irradiation, a known cause of skin cancer. Single-stranded DNA activates [http://en.wikipedia.org/wiki/RecA RecA], which in turn represses the C1 repressor protein, thus allowing the transcription of the PL promoter (Kobayashi, 2004). The PL promoter is on the biofilm-forming output plasmid (pBFR), which controls biofilm formation (Fig. 1). The biofilm protection, when part of a toggle switch mechanism, lasted indefinitely when pulsed with UV light at 8 J/m2 for only 2 seconds (Kobayashi, 2004) (Fig. 2). The biofilm-production mechanism has not been engineered in eukaryotic cells, but a prokaryote-based system that could aid with human skin disease is imaginable. For example, a sunscreen that changes colors when the DNA damage may be too intense for a sunbather as a warning, or a sunscreen that becomes more protective as the possibility of genetic damage increased. Further research should focus on how to best implement the biofilm producing output module to protect human DNA.

<center>

{|
|-
|[[Image:Toggleswitch.JPG]]
|[[Image:UVinducedbiofilm.JPG]]
|-
|'''Fig. 1.''' The RecA / traA toggleswitch mechanism. (Kobayashi, 2004 - Permission Pending)
|'''Fig. 2.''' Crystal violet absorbance measures biofilm formation. (Kobayashi, 2004 - Permission Pending)
|}
</center>

'''Treatment'''

Synthetic biologists consider finding a cure for cancer a reachable goal. Previous to the development of synthetic biology as a field, it was known that three types of bacteria, ''Bifodobacterium'', ''Clostridium'', and ''Salmonella'' all preferentially infect the dense cells of tumors (Pawelek, 2003). All three types of bacteria are associated with reducing tumor size in patients who are infected with them, and it was noted that all three bacteria act anaerobically. Capitalizing on the idea that tumors create an unusually dense, anaerobic mass of cells, Christopher Voigt’s lab engineered bacteria to identify and invade cancer cells. They utilized the ''lux'' quorum sensing from ''Vibrio fischeri'' to identify cells growing at high densities (Fig. 3) and the ''fdhF'' promoter to identify cells growing in anaerobic conditions ''in vitro'' (Fig. 4) (Anderson, 2006). They coupled this identification mechanism with the invasin output module from ''Yersinia pseudotuberculosis'' so the ''E. coli'' could invade specific cells.

<center>

{|
|-
|[[Image:InvasinDensity.JPG]]
|[[Image:InvasinAnaerobic2.JPG]]
|-
|'''Fig. 3.''' The red line represents when the invasin output module was paired with the inducible promoter ''lux'' which senses high cell density, the black line is constitutively on invasin. (Anderson, 2006 - Permission Pending)
|'''Fig. 4.''' The invasin output module was paired with the inducible promoter ''fdhF'' which senses anaerobic conditions. (Anderson, 2006 - Permission Pending)
|}

</center>

Next, scientists hope to insert a gene that would destroy the invaded cells, making the complete cycle a search, invade, and destroy loop that would only be sensitive to cancer cells. One promising destroy mechanism has been implemented in humans via intratumoral injection, based on ''Salmonella'' that contains the ''E. coli'' cytosine deaminase gene that converts 5-fluorocytosine to 5-fluorouracil, a chemotherapy drug (Nemunaitis, 2003). Unfortunately, while the tumors did produce [http://en.wikipedia.org/wiki/Fluorouracil 5-fluorouracil] (5-FU), they did not produce it in high enough yields to cause regression of the cancer. Another experiment flooded mouse cells via intratumoral injection with 6-methylpurine-2’deoxyribose (6-MPDR), which was converted to the toxin [http://jpet.aspetjournals.org/cgi/content/abstract/304/3/1280 6-methyl purine] (MeP) by the enzyme purine nucleoside phosphorylase (PNP), which is naturally found in ''E. coli'' (Critchley, 2004) (Fig. 5).

<center>

[[Image:Tumor.JPG]]

'''Fig. 5.''' Tumor size after injections. Cancer cells were injected into mice, and 5 days later injections with PBS, 6-MPDR, and invasin-enhanced ''E-coli'' began. Tumor cells recieving injections of invasin-enhanced ''E.coli'' and 6-MPDR grew the slowest, but did not get smaller. (Critchley, 2004 - Permission Pending)

</center>

The problem with this is that even though MeP was injected into the tumor, all cells in the body might be exposed to the toxin. In addition to the search and destroy mechanism, scientists hope to increase selectivity by combining the hypoxia-sensing and density-sensing units so that ''E. coli'' will only enter eukaryotic cells that exhibit both characteristics. This would make it more likely that invasin-enhanced ''E. coli'' would only invade cancer cells rather than muscle tissue that had low oxygen due to exercise (Anderson, 2006). To create a fully functional, synthetic biology-based approach to cancer therapy, scientists need to develop bacteria that discriminately invades cancer cells and destroys them. Most of the pieces are there – scientists have engineered ''E. coli'' that selectively invades hypoxic cells or dense cells and ''Salmonella'' that can destroy tumors in vivo – they just need to be put together effectively.

Finally, synthetic biology has made its mark on the field of medicine by creating drugs that are less expensive to produce than those made by pharmaceutical companies. Jay Keasling’s project involving the antimalarial drug artemisinin is the best example. Yeast was engineered to increase normal farnesyl pyrophosphate (FPP) production, convert FPP to amorphadiene, and oxidizing amorphadiene to artemisinic acid, which can then easily be separated from the yeast cells and converted to artemisinin in the laboratory (Fig. 6). This innovation allows the production of artemisinin at lower prices than those currently on the market, and without regard to environmental constraints (Ro, 2006). On a completely different tangent, Collins’s lab engineered T7 bacteriophages to deliver biofilm-degrading enzymes such as dispersin B after infecting and replicating in an ''E. coli'' biofilm. Dispersin B works to degrade an adhesin that is critical for biofilm formation. Phages that released dispersin B were found to be 99.997% effective at removing biofilm, which is 4.5 orders of magnitude higher than non-enzymatic phages, and caused a 3.65log10 reduction in bacterial cells recovered from biofilmwhen compared to untreated biofilms (Lu, 2007) (Fig. 7). The specificity of phages for a certain type of bacteria make them a viable vector to be used in human treatments, which may range from removing dental plaques to cleaning inserted medical devices. Before phages are ready for use in medicine, however, a well-characterized phage library must be created so that way any specific biofilm can be targeted using the dispersin B method. This also requires scientists being able to identify the type of bacteria in the biofilm, which may be growing inside the body. Using synthetic biology to engineer an antimalarial drug in yeast or a biofilm-destroying enzyme in bacteriophages greatly increases the possible contributions to the field of medicine.

<center>

{|
|-
|[[Image:Artemesinin.JPG]]
|[[Image:BiofilmDegradation.JPG]]
|-
|'''Fig. 6.''' All the steps in the box indicate steps that were optimized in yeast for a maximum artemisinic acid output. (Ro, 2006 - Permission Pending)
|'''Fig. 7.''' The amount of biofilm was decreased when the dispersin B enzymes were incorporated into T7 bacteriophages. (Lu, 2007 - Permission Pending)
|}
</center>

'''Conclusion'''

Medical applications of synthetic biology are wide-ranging and eminently applicable to daily life. Synthetic biology based cancer treatments that utilize ''E.coli'' engineered to sense and attack tissues with high cell density and low oxygen availability are promising and need to be tested in vivo. Designing vectors out of attenuated bacteria may be a safe, effective method for disease treatment, but need to be able to reach a target tissue if administered orally. Using biofilms to sense sun damage potential may decrease the likelihood of skin cancer. Finally, using engineered cells to produce medicines in a cost-effective, environmentally-friendly way may revolutionize the pharmaceutical industry. Investing more research efforts in the field of synthetic biology may turn out to be an investment in medical technology.

== Works Cited ==

-[http://www.bio.davidson.edu/courses/synthetic/papers/2006_anderson.pdf Anderson JC, Clarke EJ, Arkin, AP, and Voigt CA (2006). Environmentally controlled invasion of cancer cells by engineered bacteria. Journal of Molecular Biology 355:619-27.]

-[http://www.nature.com/gt/journal/v11/n15/abs/3302281a.html Critchley RJ, Jezzard S, Radford KJ, Goussard S, Lemoine NR, et al. (2004). Potential therapeutic applications of recombinant, invasive ''E. coli''. Gene Therapy 11:1224-33.]

-[http://www.ncbi.nlm.nih.gov/sites/entrez?db=pubmed&uid=15203915&cmd=showdetailview&indexed=google Garmory HS, Leary SEC, Griffin, KF, Williamson D, Brown, KA, and Titball RW (2003). The use of live attenuated bacteria as a delivery system for heterologous antigens. Journal of Drug Targeting 11:471-79.]

-[http://www.pnas.org/cgi/reprint/101/22/8414 Kobayashi H, et al. (2004). Programmable cells: Interfacing natural and engineered gene networks. PNAS 101: 8414-19.]

-[http://www.bio.davidson.edu/courses/synthetic/papers/Phage_BioFilms.pdf Lu, TK, and Collins JJ (2007). Dispersing biofilms with engineered enzymatic bacteriophage. PNAS 104: 11197-11202.]

-[http://www.nature.com/cgt/journal/v10/n10/abs/7700634a.html Nemunaitis J, Cunningham C, Senzer N, Kuhn J, Cramm J, Litz C, et al. (2003). Pilot trial of genetically modified, attenuated ''Salmonella'' expressing cytosine deaminase gene in refractory cancer patients. Cancer Gene Therapy 10:737-744.]

-[http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6W85-49FGKHC-N&_user=10&_coverDate=09%2F30%2F2003&_rdoc=1&_fmt=&_orig=search&_sort=d&view=c&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=d4529b45974bfe7d9ddab1fc865f088b Pawelek JM, Low KB, and Bermudes D (2003). Bacteria as tumour-targeting vectors. Lancet Oncology 4:548-56.]

-[http://www.bio.davidson.edu/courses/synthetic/papers/Keasling_malaria.pdf Ro D, et al. (2006). Production of the antimalarial drug precursor artemisinic acid in engineered yeast. Nature 440:940-43.]

Logic Gates - Emma Garren

2007-12-12T21:54:53Z

WikiSysop: /* Logic Gates in Synthetic Biology */

=Background: Logic Gates and Truth Tables=

A [http://en.wikipedia.org/wiki/Logic_gate logic gate], the building block for digital circuits, is a computing unit that performs a logical operation on one or more inputs and produces a single output. Gates are identified by their function, and each type of logic gate can be represented with a distinctive symbol. Inputs to outputs are read from left to right. A small circle on the right indicates the inversion of the output. A double line on the left indicates that the function is "exclusive."

A [http://en.wikipedia.org/wiki/Truth_table truth table] is a useful way to describe the behavior or function of a logic gate. Logic states for both inputs and outputs are designated with 0's and 1's. Click [[Logic Gates: Symbols and Truth Tables|here]] to see some common logic gates and their truth tables.

Logic gates can be placed in parallel or serial combinations in order to perform more complex functions. Here, two parallel outputs, D and E, act as inputs to the function of output Q:

Figure 1. Combination of logic gates: Q = D OR E = (NOT(A OR B)) OR (B AND C)

[[Image:combination_gates.gif]]

([http://www.kpsec.freeuk.com/gates.htm Image] by John Hewes, 2007)

=Logic Gates in Synthetic Biology=
Synthetic biology applies many of the principles of engineering to the field of biology in order to create biological devices which can ultimately be integrated into increasingly complex systems. These principles include standardization of parts, modularity, abstraction, reliability, predictability, and uniformity (Adrianantoandro ''et al.'', 2006). The application of engineering principles to biology is complicated by the inability to predict the functions of even simple devices and modules within the cellular environment. Some of the confounding factors are [[Term paper wiki|gene expression noise]], mutation, cell death, undefined and changing extracellular environments, and interactions with the cellular context (Adrianantoandro ''et al.'', 2006).

While for digital logic, inputs are either on or off (1 or 0), biological logic is sometimes leads to intermediate induction levels (Voigt, 2006). However, due to their [http://en.wikipedia.org/wiki/Sigmoid_function sigmoid-shaped] dose response curves, gene regulation systems can be considered genetic analog-digital converters. The signal is either ON or OFF for a wide range of input concentrations, with the system changing between the ON and OFF states in a relatively small concentration window (Kramer ''et al.'', 2004).

In synthetic biology, logic gates are created by engineering the biochemical reactions that regulate various cellular processes, such as transcription, translation, protein phosphorylation, allosteric regulation, ligand/receptor binding, and enzymatic reactions. Although the diversity of biochemical reactions can make it difficult to combine different devices, these logic gates can be used to build complex systems with functions that have many practical applications. [http://gcat.davidson.edu/GcatWiki/index.php/CellularMemory:Mathematical_Models Mathematical modeling] is used to predict the dynamics of the signaling and regulatory networks resulting from the logic gates.

=Biomolecular Logic Gates:'' In Vitro''=

''In vitro'' studies have been used to design combinations of molecules that have emergent properties related to information processing--molecular computing devices. Both the inputs and outputs consist of molecular species, with the output being a biologically active molecule. The extent to which these devices will be used with the cellular context is unclear--however, they are bound to inspire new directions for research in synthetic biology, and have potential applications in biochemical sensing, pathway engineering, and medical diagnosis and treatment.

==Protein-based Logic Gates==

Enzymes:
*Two coupled enzymes perform in parallel the 'AND' and 'InhibAND' logic gate operations. (Baron et al., 2006) Summary:
*Logic Gates and Elementary Computing by Enzymes. (Baron et al., 2006) Summary:

Synthetic Peptide Networks:
*Boolean Logic Functions of a Synthetic Peptide Network. (Ashkenasy and Ghadiri, 2004) Summary:

Signaling Proteins:
*Engineering synthetic signaling proteins with ultrasensitive input/output control. (Dueber et al., 2007) Summary: Many eukaryotic signaling proteins have natural, modular "input" and "output" domains: the "inputs" participate in steric or conformational autoinhibitory reactions, and the "outputs" are catalytically, constitutively active domains. Simple synthetic switch functions can be engineered by swapping the regulatory and catalytic domains. This paper describes the engineering of "ultrasensitive switches" for use in more complex regulatory networks, by combining multiple identical modular autoinhibitory domains that function [http://en.wikipedia.org/wiki/Cooperativity cooperatively]. Mathematical models are used to simulate the behavior of these multivalent domain switches and explore the effect of autoinhibitory interaction number and affinity: an external input ligand alters the population distribution of active vs. inactive enzymes, where individual states are "fully repressed in the presence of any intramolecular interactions and fully active only in the absence of all intramolecular interactions" (661). The predictions from the models are tested using synthetic switches built by linking the catalytic output domain of the protein N-WASP to novel peptide input, demonstrating that it is possible to engineer nonlinear switches based on the cooperativity of simple autoinhibitory components. Other complex switches, such as ones that integrate three input signals, have also been built using this approach.

==Deoxyribozyme-based Logic Gates==

Deoxyribozyme-based gates are controlled by oligonucleotide inputs, and have been used to engineer logic gates that perform:
*multiple logical operations in parallel
*single-step signaling cascades
*a feedback cycle that acts as an exponential chain reaction

These papers provide examples of deoxyribozyme-based logic gates:
*Deoxyribozyme-Based Ligase Logic Gates and Their Initial Circuits. (Stojanovic et al., 2005) Summary:
*Deoxyribozyme-Based Three-Input Logic Gates and Construction of a Molecular Full Adder. (Lederman et al., 2006) Summary:
*Construction of Molecular Logic Gates with a DNA-Cleaving Deoxyribozyme (Chen et al., 2006) Summary:

==DNA-based Logic Gates==

*Modular Multi-Level Circuits from Immobilized DNA-Based Logic Gates. (Frezza et al., 2007) Summary:
*DNA Logic Gates Based on Structural Polymorphism of Telomere DNA Molecules Responding to Chemical Input Signals. (Miyoshi et al., 2006) Summary:
*Photonic boolean logic gates based on DNA aptamers. (Yoshida and Yokobayashi, 2006) Summary:
*Parallel Molecular Computations of Pairwise Exclusive-OR (XOR) Using DNA "String Tile" Self-Assembly. (Yan et al., 2003) Summary:

==An autonomous molecular computer for logical control of gene expression. (Benenson et al., 2004)==

===Overview:===

This gate uses sequence recognition to control enzyme catalysis of covalent bond formation and breakage, producing an ssDNA output.

*Input: Specific combination mRNA levels, serving as a simple model of a disease state (cancer)
*Output: “Drug” (or drug repressor) in the form of a single-stranded DNA sequence with known anti-cancer activity

===Design:===

[[Figure 2. Logical design of the molecular computer.]]

An ''in vitro'' system is designed to recognize a specific combination of mRNA levels as its inputs, and performs a logical operation that results in the production of a molecule that can affect gene expression. The input mRNA levels have been designed to mimic a simplistic version of gene expression modeling cancer, and the output is a drug-like ssDNA with known anticancer activity. Therefore, the molecular computer is analogous to "a computational version of 'diagnosis'" (424).

===Function:===

[[Figure 3. Operation of the molecular computer.]]

The molecular computers consist of a double-stranded DNA sequence with unique 7-bp sequences that recognize the “input” RNA. Each mRNA indicator is processed one at a time. An “inactivation tag” results in the displacement of the transition molecule, which destroys the computation fragment. An “activation tag” results in activation of the transition molecule. There are two separate molecular computers working simultaneously—one that releases the drug (a specific single-stranded DNA sequence) upon “positive diagnosis,” and another that releases the drug suppressor in response to “negative diagnosis.” The drug (or drug repressor) is incorporated into the DNA fragment as an inactive loop, protected by the double-stranded recognition sequences. If all of the transitions are “positive,” the ultimate output is drug administration (release of ssDNA). If any of the steps results in a “negative” transition, the output is a drug repressor. The design is flexible in that any sufficiently long mRNA molecule can be used as an indicator, and any ssDNA or short RNA molecule (up to at least 21-bp) can be designed as the output. PCR was used to for the experimental demonstration of both diagnosis and drug administration, and it is shown that the amount of active drug increases with the confidence of positive diagnosis. Future work could test the effectiveness of using alternate inputs (such as proteins), alternate outputs (such as RNA interference), as well as testing the function of the molecular computer ''in vivo''.

==Enzyme-Free Nucleic Acid Logic Circuits. (Seelig et al., 2006)==

Overview:

*Input: 2 DNA strands - F(in) and G(in)
*Gate: 3 DNA strands - E(q), F(f), and G
*Output: Fluorescence - release of F(f)

Nucleic acid devices are simplified by the predicability of base pairing. Although previous research has engineered nucleic acid logic switches based on hybridization and conformational changes ''in vivo'', and this paper designs chemical logic gates that are capable of being combined into large, reliable circuits. These logic gates embody the following digital design principles: logic, cascading, restoration, fan-out, and modularity.

The benefits of this approach are that both inputs and outputs are in the same form, which makes cascading possible (the output for one gate serves as the input for the next gate in the circuit).

Gate function is entirely determined by base pairing and breaking. Each gate is composed of one or more gate strands, and one output strand.

[[Figure 4. Two-input AND gate.]]

Restoration:
*When a gate fails to produce enough output when triggered, restoration increases a moderate output amount to the full activation level.
*When a gate "leaks" by spontaneously releasing the output strand, restoration decreases the small output amount to a negligible level.
*Gates for [[Restoration: Amplification Gate|amplification]] and [[Restoration: Thresholding Gate|thresholding]] were used to implement signal restoration.

Modularity and Scalability:
*Eleven gates (AND, OR, sequence translation, input amplification, and signal restoration) were used to compose a large, complex circuit.

=Cellular Logic Gates: ''In Vivo''=

As stated above, there the cellular environment possesses many characteristics that complicate the implementation of biomolecular logic circuits, such as gene expression noise, mutation, cell death, undefined and changing extracellular environments, and interactions with the cellular context. Some researchers are trying to engineer the "minimal cell," either through top-down or bottom-up approaches, in order to produce a living unit that can perform logical operations without some of these excess confounding factors found in the cells used . These efforts were recently reviewed by [http://www.nature.com/msb/journal/v2/n1/pdf/msb4100090.pdf Forster and Church (2006)]. However, many efforts at building cellular logic gates have succeeded, both in prokaryotes and eukaryotes, with wide-ranging applications. Below are summaries of a few examples.

==Synthetic Oscillators and Switches==
[[Image:design_fig_1ab.gif|Synthetic oscillators and switches.]]

[[Image:design_fig_1d.gif]]

(Image from Figure 1 of Drubin et al., 2007. Permission pending.)

Figure 5. Schematic representation of the function of various engineered biological switches.

A. [[Repressilator]] (Elowitz and Leibler, 2000). The "repressilator," or synthetic cellular oscillator, can be built from a string of three repressors, each acting the repress the expression of the next gene in the circuit. Oscillatory output is read by GFP expression regulated by one of the repressors (in this case, tetracycline). The design is analogous to a series of three NOT gates.

B. Toggle switch (Gardner et al., 2000). A toggle switch can be designed from two repressor systems that cross-regulate each other's promoters, and is analogous to the construction of two independent IF gates. It is useful as a pathway module to create more complex programmable cells. Toggle switches can be used to engineer [[CellularMemory:Toggle_Switch|cellular memory]].

D. RNA-based antiswitch. When the engineered RNA is bound to an inducer ligand, the structure changes to either expose or hide a region of RNA that is homologous to a region of the target mRNA (encompassing the translational start site). Thus, when the antisense region is exposed, translation of target mRNA is repressed. Synthetic switches in both eukaryotes ([[Antiswitches|antiswitches]]) and prokaryotes ([[Riboregulators|riboregulators]] and [[Riboswitches|riboswitches]]) can be mediated via RNA devices.

==Environmental signal integration by a modular AND gate. (Anderson et al., 2007)==

AND gates allow cells to integrate multiple signals and can increase their specificity in sensing the environment. This is especially useful for engineering cells to sense environments that are not encountered naturally, or those that are too specific to be identified by a single environmental signal.

A logic gate achieves modularity when it can be used as a self-contained component of more complex systems, and is designed to interface with multiple different inputs and outputs. This paper demonstrates a modular AND gate in ''E. coli'' that uses promoters for both the inputs and the output. This design, unlike that of previously engineered prokaryotic logic gates, makes it relatively easy to use the same gate for different inputs and outputs.

Overview:
*Inputs:
# Promoter that drives the expression of the gene for T7 RNA polymerase (''T7ptag''), containing two [http://en.wikipedia.org/wiki/Stop_codon amber stop codons] that prevent translation under normal circumstances.
#Promoter that drives the expression of the gene for the SupD amber suppressor (''supD''), which decodes the amber stop codons as serine, and allows for translation of T7 RNA polymerase.
*Output: Gene expressed under the T7 promoter, which requires functional translation of both T7 RNA polymerase and the SupD amber suppressor.

The fluorescence data (see [Figure 2. Function of the AND gate. Figure 2]) was used to parameterize a [[transfer function model]] that was derived in order to understand how the range of the input promoters affects the function of the circuit.

[[Figure 1. Schematic representation of AND gate.]]

*Input 1: P(sal) controls the expression supD gene, and is activated by the addition of salicylate (Sal).
*Input 2: P(BAD) controls T7 RNA polymerase gene, and is activated by the addition of arabinose (Ara). Because a strong RBS for T7ptag resulted in high levels of basal expression, the RBS was mutagenized and tuned by screening for the presence of output only when both inducers were present.
*Output: ''GFPmut3_LAA'' (fast-folding GFP with a degradation tag)

[[Figure 2. Function of the AND gate.]]

There is 1000-fold induction of fluorescence in the presence of high concentrations of both inducers, arabinose and salicylate.

[[Figure 6. Modularity of the AND gate.]]

The modularity of the AND gate is demonstrated by reconnecting the gate to new inputs (natural promoters), and a new output (desired phenotype).

A. Exchanging the inputs:
*Input 1: The ''lux'' promoter, which responds to the [[Quorum Sensing|quorum signal]] AI-1.
*Input 2: The ''mgrB'' promoter, which responds to the absence of exogenous magnesium via the PhoPQ two-component system.

B. Exchanging the output:
*Output: The ''inv'' gene, coding for invasin, a protein that allows bacteria to invade mammalian cells.

==BioLogic Gates Enable Logical Transcription Control in Mammalian Cells. (Kramer et al., 2004)==

Transcription control modules, responsive to up to three small molecule inputs, were engineered in mammalian Chinese hamster ovary cells. These "BioLogic gates" provide the tools and building blocks to engineer more complex gene regulatory networks in eukaryotic cells. The gates use butyrolactone-, streptogramin-, tetracycline-, and macrolide-dependent transcription factors, each fused to a KRAB or VP16 repression domain.

#[[NOT IF gate]] - two inputs
#[[NOT IF IF gate]] - three inputs
#[[NAND gate]] - parallel arrangement of two NOT gates
#[[OR gate]] - parallel arrangement of two IF gates
#[[NOR gate]] - constructed from two NOT gates in consecutive order
#[[INVERTER gate]] - combination of two independent IF gates, acts as the inverse of the NOT IF gate

==Cellular Logic with Orthogonal Ribosomes. (Rackham and Chin, 2006)==

Overview:

Two-input logic gates were constructed based on the interactions between synthetic O-rRNA and O-mRNA.
*Input: Orthogonal ribosomes (O-ribosomes), which translate O-mRNA, but do not significantly translate any of the thousands of cellular transcripts bearing cellular RBS sequences.
*Gate: Orthogonal mRNAs (O-mRNAs), which contain ribosome-binding sequences (RBSs) that do not direct the translation of downstream genes by endogenous ribosomes.
*Output: Fluorescence.

Three distinct O-ribosome-O-mRNA pairs were isolated, with molecular specificities for independent function. This was accomplished through [[Hunter Stone - Synthetic Biology Seminar|directed evolution]] in two steps:
#A library of new potential RBSs were placed upstream of a novel fusion of the genes encoding chloramphenicol acetyltransferase and uracil phosphoribosyltransferase, and screened against 5-fluorouracil to select for the O-mRNA sequences that were not translated by endogenous ribosomes.
#The selected O-mRNAs were combined with a library of mutated 16s rRNA sequences, and these cells were grown in the presence of chloramphenicol to screen for those in which the mutant ribosomes translated the O-mRNAs.
The O-ribosome-O-mRNA pairs can be used to control almost any molecular interaction that can be linked to gene expression. In this case they were used to build an AND gate composed of two O-mRNA sequences: O-mRNA-A-omega, encoding the omega fragment of beta-galactosidase, and O-mRNA-C-alpha, encoding the alpha fragment of beta-galactosidase. Synthesis and assembly of a complete beta-galactosidase enzyme (both fragments) results in the cells hydrolyzing FDG into F (fluorescein), which is detected with a fluorometer. Cells programmed with both of the corresponding O-ribosome inputs, O-rRNA-A and O-rRNA-C, exhibited a 20-fold increase in fluorescence when compared with cells containing any other rRNA combinations.

[[Figure 1. O-ribosomes and Boolean logic.]]

=Applications and Future Directions=

Logic gates can be used to design increasingly complex circuits with far-reaching applications in:
*Genetic engineering
*Nanotechnology
*Industrial Fermentation
*Metabolic engineering:
**increasingly complex synthetic gene circuits might be used to engineer and optimize novel metabolic pathways.
*[[Medical Applications of Synthetic Biology - Samantha Simpson|Medicine]]:
**Bacteria to deliver cancer treatment: the integration of multiple inputs can help bacteria sense and respond to increasingly specific environments, such as that of a tumor in the human body.
**Pharmaceuticals: produced through metabolic engineering; "smart" drug delivery.

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Logic Gates - Emma Garren

2007-12-12T21:54:01Z

WikiSysop: /* Logic Gates in Synthetic Biology */

=Background: Logic Gates and Truth Tables=

A [http://en.wikipedia.org/wiki/Logic_gate logic gate], the building block for digital circuits, is a computing unit that performs a logical operation on one or more inputs and produces a single output. Gates are identified by their function, and each type of logic gate can be represented with a distinctive symbol. Inputs to outputs are read from left to right. A small circle on the right indicates the inversion of the output. A double line on the left indicates that the function is "exclusive."

A [http://en.wikipedia.org/wiki/Truth_table truth table] is a useful way to describe the behavior or function of a logic gate. Logic states for both inputs and outputs are designated with 0's and 1's. Click [[Logic Gates: Symbols and Truth Tables|here]] to see some common logic gates and their truth tables.

Logic gates can be placed in parallel or serial combinations in order to perform more complex functions. Here, two parallel outputs, D and E, act as inputs to the function of output Q:

Figure 1. Combination of logic gates: Q = D OR E = (NOT(A OR B)) OR (B AND C)

[[Image:combination_gates.gif]]

([http://www.kpsec.freeuk.com/gates.htm Image] by John Hewes, 2007)

=Logic Gates in Synthetic Biology=
Synthetic biology applies many of the principles of engineering to the field of biology in order to create biological devices which can ultimately be integrated into increasingly complex systems. These principles include standardization of parts, modularity, abstraction, reliability, predictability, and uniformity (Adrianantoandro ''et al.'', 2006). The application of engineering principles to biology is complicated by the inability to predict the functions of even simple devices and modules within the cellular environment. Some of the confounding factors are [[Term paper wiki|gene expression noise]], mutation, cell death, undefined and changing extracellular environments, and interactions with the cellular context (Adrianantoandro ''et al.'', 2006).

While for digital logic, inputs are either on or off (1 or 0), biological logic is sometimes leads to intermediate induction levels (Voigt, 2006). However, due to their [http://en.wikipedia.org/wiki/Sigmoid_function sigmoid-shaped] dose response curves, gene regulation systems can be considered genetic analog-digital converters. The signal is either ON or OFF for a wide range of input concentrations, with the system changing between the ON and OFF states in a relatively small concentration window (Kramer et al., 2004).

In synthetic biology, logic gates are created by engineering the biochemical reactions that regulate various cellular processes, such as transcription, translation, protein phosphorylation, allosteric regulation, ligand/receptor binding, and enzymatic reactions. Although the diversity of biochemical reactions can make it difficult to combine different devices, these logic gates can be used to build complex systems with functions that have many practical applications. [http://gcat.davidson.edu/GcatWiki/index.php/CellularMemory:Mathematical_Models Mathematical modeling] is used to predict the dynamics of the signaling and regulatory networks resulting from the logic gates.

=Biomolecular Logic Gates:'' In Vitro''=

''In vitro'' studies have been used to design combinations of molecules that have emergent properties related to information processing--molecular computing devices. Both the inputs and outputs consist of molecular species, with the output being a biologically active molecule. The extent to which these devices will be used with the cellular context is unclear--however, they are bound to inspire new directions for research in synthetic biology, and have potential applications in biochemical sensing, pathway engineering, and medical diagnosis and treatment.

==Protein-based Logic Gates==

Enzymes:
*Two coupled enzymes perform in parallel the 'AND' and 'InhibAND' logic gate operations. (Baron et al., 2006) Summary:
*Logic Gates and Elementary Computing by Enzymes. (Baron et al., 2006) Summary:

Synthetic Peptide Networks:
*Boolean Logic Functions of a Synthetic Peptide Network. (Ashkenasy and Ghadiri, 2004) Summary:

Signaling Proteins:
*Engineering synthetic signaling proteins with ultrasensitive input/output control. (Dueber et al., 2007) Summary: Many eukaryotic signaling proteins have natural, modular "input" and "output" domains: the "inputs" participate in steric or conformational autoinhibitory reactions, and the "outputs" are catalytically, constitutively active domains. Simple synthetic switch functions can be engineered by swapping the regulatory and catalytic domains. This paper describes the engineering of "ultrasensitive switches" for use in more complex regulatory networks, by combining multiple identical modular autoinhibitory domains that function [http://en.wikipedia.org/wiki/Cooperativity cooperatively]. Mathematical models are used to simulate the behavior of these multivalent domain switches and explore the effect of autoinhibitory interaction number and affinity: an external input ligand alters the population distribution of active vs. inactive enzymes, where individual states are "fully repressed in the presence of any intramolecular interactions and fully active only in the absence of all intramolecular interactions" (661). The predictions from the models are tested using synthetic switches built by linking the catalytic output domain of the protein N-WASP to novel peptide input, demonstrating that it is possible to engineer nonlinear switches based on the cooperativity of simple autoinhibitory components. Other complex switches, such as ones that integrate three input signals, have also been built using this approach.

==Deoxyribozyme-based Logic Gates==

Deoxyribozyme-based gates are controlled by oligonucleotide inputs, and have been used to engineer logic gates that perform:
*multiple logical operations in parallel
*single-step signaling cascades
*a feedback cycle that acts as an exponential chain reaction

These papers provide examples of deoxyribozyme-based logic gates:
*Deoxyribozyme-Based Ligase Logic Gates and Their Initial Circuits. (Stojanovic et al., 2005) Summary:
*Deoxyribozyme-Based Three-Input Logic Gates and Construction of a Molecular Full Adder. (Lederman et al., 2006) Summary:
*Construction of Molecular Logic Gates with a DNA-Cleaving Deoxyribozyme (Chen et al., 2006) Summary:

==DNA-based Logic Gates==

*Modular Multi-Level Circuits from Immobilized DNA-Based Logic Gates. (Frezza et al., 2007) Summary:
*DNA Logic Gates Based on Structural Polymorphism of Telomere DNA Molecules Responding to Chemical Input Signals. (Miyoshi et al., 2006) Summary:
*Photonic boolean logic gates based on DNA aptamers. (Yoshida and Yokobayashi, 2006) Summary:
*Parallel Molecular Computations of Pairwise Exclusive-OR (XOR) Using DNA "String Tile" Self-Assembly. (Yan et al., 2003) Summary:

==An autonomous molecular computer for logical control of gene expression. (Benenson et al., 2004)==

===Overview:===

This gate uses sequence recognition to control enzyme catalysis of covalent bond formation and breakage, producing an ssDNA output.

*Input: Specific combination mRNA levels, serving as a simple model of a disease state (cancer)
*Output: “Drug” (or drug repressor) in the form of a single-stranded DNA sequence with known anti-cancer activity

===Design:===

[[Figure 2. Logical design of the molecular computer.]]

An ''in vitro'' system is designed to recognize a specific combination of mRNA levels as its inputs, and performs a logical operation that results in the production of a molecule that can affect gene expression. The input mRNA levels have been designed to mimic a simplistic version of gene expression modeling cancer, and the output is a drug-like ssDNA with known anticancer activity. Therefore, the molecular computer is analogous to "a computational version of 'diagnosis'" (424).

===Function:===

[[Figure 3. Operation of the molecular computer.]]

The molecular computers consist of a double-stranded DNA sequence with unique 7-bp sequences that recognize the “input” RNA. Each mRNA indicator is processed one at a time. An “inactivation tag” results in the displacement of the transition molecule, which destroys the computation fragment. An “activation tag” results in activation of the transition molecule. There are two separate molecular computers working simultaneously—one that releases the drug (a specific single-stranded DNA sequence) upon “positive diagnosis,” and another that releases the drug suppressor in response to “negative diagnosis.” The drug (or drug repressor) is incorporated into the DNA fragment as an inactive loop, protected by the double-stranded recognition sequences. If all of the transitions are “positive,” the ultimate output is drug administration (release of ssDNA). If any of the steps results in a “negative” transition, the output is a drug repressor. The design is flexible in that any sufficiently long mRNA molecule can be used as an indicator, and any ssDNA or short RNA molecule (up to at least 21-bp) can be designed as the output. PCR was used to for the experimental demonstration of both diagnosis and drug administration, and it is shown that the amount of active drug increases with the confidence of positive diagnosis. Future work could test the effectiveness of using alternate inputs (such as proteins), alternate outputs (such as RNA interference), as well as testing the function of the molecular computer ''in vivo''.

==Enzyme-Free Nucleic Acid Logic Circuits. (Seelig et al., 2006)==

Overview:

*Input: 2 DNA strands - F(in) and G(in)
*Gate: 3 DNA strands - E(q), F(f), and G
*Output: Fluorescence - release of F(f)

Nucleic acid devices are simplified by the predicability of base pairing. Although previous research has engineered nucleic acid logic switches based on hybridization and conformational changes ''in vivo'', and this paper designs chemical logic gates that are capable of being combined into large, reliable circuits. These logic gates embody the following digital design principles: logic, cascading, restoration, fan-out, and modularity.

The benefits of this approach are that both inputs and outputs are in the same form, which makes cascading possible (the output for one gate serves as the input for the next gate in the circuit).

Gate function is entirely determined by base pairing and breaking. Each gate is composed of one or more gate strands, and one output strand.

[[Figure 4. Two-input AND gate.]]

Restoration:
*When a gate fails to produce enough output when triggered, restoration increases a moderate output amount to the full activation level.
*When a gate "leaks" by spontaneously releasing the output strand, restoration decreases the small output amount to a negligible level.
*Gates for [[Restoration: Amplification Gate|amplification]] and [[Restoration: Thresholding Gate|thresholding]] were used to implement signal restoration.

Modularity and Scalability:
*Eleven gates (AND, OR, sequence translation, input amplification, and signal restoration) were used to compose a large, complex circuit.

=Cellular Logic Gates: ''In Vivo''=

As stated above, there the cellular environment possesses many characteristics that complicate the implementation of biomolecular logic circuits, such as gene expression noise, mutation, cell death, undefined and changing extracellular environments, and interactions with the cellular context. Some researchers are trying to engineer the "minimal cell," either through top-down or bottom-up approaches, in order to produce a living unit that can perform logical operations without some of these excess confounding factors found in the cells used . These efforts were recently reviewed by [http://www.nature.com/msb/journal/v2/n1/pdf/msb4100090.pdf Forster and Church (2006)]. However, many efforts at building cellular logic gates have succeeded, both in prokaryotes and eukaryotes, with wide-ranging applications. Below are summaries of a few examples.

==Synthetic Oscillators and Switches==
[[Image:design_fig_1ab.gif|Synthetic oscillators and switches.]]

[[Image:design_fig_1d.gif]]

(Image from Figure 1 of Drubin et al., 2007. Permission pending.)

Figure 5. Schematic representation of the function of various engineered biological switches.

A. [[Repressilator]] (Elowitz and Leibler, 2000). The "repressilator," or synthetic cellular oscillator, can be built from a string of three repressors, each acting the repress the expression of the next gene in the circuit. Oscillatory output is read by GFP expression regulated by one of the repressors (in this case, tetracycline). The design is analogous to a series of three NOT gates.

B. Toggle switch (Gardner et al., 2000). A toggle switch can be designed from two repressor systems that cross-regulate each other's promoters, and is analogous to the construction of two independent IF gates. It is useful as a pathway module to create more complex programmable cells. Toggle switches can be used to engineer [[CellularMemory:Toggle_Switch|cellular memory]].

D. RNA-based antiswitch. When the engineered RNA is bound to an inducer ligand, the structure changes to either expose or hide a region of RNA that is homologous to a region of the target mRNA (encompassing the translational start site). Thus, when the antisense region is exposed, translation of target mRNA is repressed. Synthetic switches in both eukaryotes ([[Antiswitches|antiswitches]]) and prokaryotes ([[Riboregulators|riboregulators]] and [[Riboswitches|riboswitches]]) can be mediated via RNA devices.

==Environmental signal integration by a modular AND gate. (Anderson et al., 2007)==

AND gates allow cells to integrate multiple signals and can increase their specificity in sensing the environment. This is especially useful for engineering cells to sense environments that are not encountered naturally, or those that are too specific to be identified by a single environmental signal.

A logic gate achieves modularity when it can be used as a self-contained component of more complex systems, and is designed to interface with multiple different inputs and outputs. This paper demonstrates a modular AND gate in ''E. coli'' that uses promoters for both the inputs and the output. This design, unlike that of previously engineered prokaryotic logic gates, makes it relatively easy to use the same gate for different inputs and outputs.

Overview:
*Inputs:
# Promoter that drives the expression of the gene for T7 RNA polymerase (''T7ptag''), containing two [http://en.wikipedia.org/wiki/Stop_codon amber stop codons] that prevent translation under normal circumstances.
#Promoter that drives the expression of the gene for the SupD amber suppressor (''supD''), which decodes the amber stop codons as serine, and allows for translation of T7 RNA polymerase.
*Output: Gene expressed under the T7 promoter, which requires functional translation of both T7 RNA polymerase and the SupD amber suppressor.

The fluorescence data (see [Figure 2. Function of the AND gate. Figure 2]) was used to parameterize a [[transfer function model]] that was derived in order to understand how the range of the input promoters affects the function of the circuit.

[[Figure 1. Schematic representation of AND gate.]]

*Input 1: P(sal) controls the expression supD gene, and is activated by the addition of salicylate (Sal).
*Input 2: P(BAD) controls T7 RNA polymerase gene, and is activated by the addition of arabinose (Ara). Because a strong RBS for T7ptag resulted in high levels of basal expression, the RBS was mutagenized and tuned by screening for the presence of output only when both inducers were present.
*Output: ''GFPmut3_LAA'' (fast-folding GFP with a degradation tag)

[[Figure 2. Function of the AND gate.]]

There is 1000-fold induction of fluorescence in the presence of high concentrations of both inducers, arabinose and salicylate.

[[Figure 6. Modularity of the AND gate.]]

The modularity of the AND gate is demonstrated by reconnecting the gate to new inputs (natural promoters), and a new output (desired phenotype).

A. Exchanging the inputs:
*Input 1: The ''lux'' promoter, which responds to the [[Quorum Sensing|quorum signal]] AI-1.
*Input 2: The ''mgrB'' promoter, which responds to the absence of exogenous magnesium via the PhoPQ two-component system.

B. Exchanging the output:
*Output: The ''inv'' gene, coding for invasin, a protein that allows bacteria to invade mammalian cells.

==BioLogic Gates Enable Logical Transcription Control in Mammalian Cells. (Kramer et al., 2004)==

Transcription control modules, responsive to up to three small molecule inputs, were engineered in mammalian Chinese hamster ovary cells. These "BioLogic gates" provide the tools and building blocks to engineer more complex gene regulatory networks in eukaryotic cells. The gates use butyrolactone-, streptogramin-, tetracycline-, and macrolide-dependent transcription factors, each fused to a KRAB or VP16 repression domain.

#[[NOT IF gate]] - two inputs
#[[NOT IF IF gate]] - three inputs
#[[NAND gate]] - parallel arrangement of two NOT gates
#[[OR gate]] - parallel arrangement of two IF gates
#[[NOR gate]] - constructed from two NOT gates in consecutive order
#[[INVERTER gate]] - combination of two independent IF gates, acts as the inverse of the NOT IF gate

==Cellular Logic with Orthogonal Ribosomes. (Rackham and Chin, 2006)==

Overview:

Two-input logic gates were constructed based on the interactions between synthetic O-rRNA and O-mRNA.
*Input: Orthogonal ribosomes (O-ribosomes), which translate O-mRNA, but do not significantly translate any of the thousands of cellular transcripts bearing cellular RBS sequences.
*Gate: Orthogonal mRNAs (O-mRNAs), which contain ribosome-binding sequences (RBSs) that do not direct the translation of downstream genes by endogenous ribosomes.
*Output: Fluorescence.

Three distinct O-ribosome-O-mRNA pairs were isolated, with molecular specificities for independent function. This was accomplished through [[Hunter Stone - Synthetic Biology Seminar|directed evolution]] in two steps:
#A library of new potential RBSs were placed upstream of a novel fusion of the genes encoding chloramphenicol acetyltransferase and uracil phosphoribosyltransferase, and screened against 5-fluorouracil to select for the O-mRNA sequences that were not translated by endogenous ribosomes.
#The selected O-mRNAs were combined with a library of mutated 16s rRNA sequences, and these cells were grown in the presence of chloramphenicol to screen for those in which the mutant ribosomes translated the O-mRNAs.
The O-ribosome-O-mRNA pairs can be used to control almost any molecular interaction that can be linked to gene expression. In this case they were used to build an AND gate composed of two O-mRNA sequences: O-mRNA-A-omega, encoding the omega fragment of beta-galactosidase, and O-mRNA-C-alpha, encoding the alpha fragment of beta-galactosidase. Synthesis and assembly of a complete beta-galactosidase enzyme (both fragments) results in the cells hydrolyzing FDG into F (fluorescein), which is detected with a fluorometer. Cells programmed with both of the corresponding O-ribosome inputs, O-rRNA-A and O-rRNA-C, exhibited a 20-fold increase in fluorescence when compared with cells containing any other rRNA combinations.

[[Figure 1. O-ribosomes and Boolean logic.]]

=Applications and Future Directions=

Logic gates can be used to design increasingly complex circuits with far-reaching applications in:
*Genetic engineering
*Nanotechnology
*Industrial Fermentation
*Metabolic engineering:
**increasingly complex synthetic gene circuits might be used to engineer and optimize novel metabolic pathways.
*[[Medical Applications of Synthetic Biology - Samantha Simpson|Medicine]]:
**Bacteria to deliver cancer treatment: the integration of multiple inputs can help bacteria sense and respond to increasingly specific environments, such as that of a tumor in the human body.
**Pharmaceuticals: produced through metabolic engineering; "smart" drug delivery.

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Logic Gates - Emma Garren

2007-12-12T21:53:21Z

WikiSysop: /* Logic Gates in Synthetic Biology */

=Background: Logic Gates and Truth Tables=

A [http://en.wikipedia.org/wiki/Logic_gate logic gate], the building block for digital circuits, is a computing unit that performs a logical operation on one or more inputs and produces a single output. Gates are identified by their function, and each type of logic gate can be represented with a distinctive symbol. Inputs to outputs are read from left to right. A small circle on the right indicates the inversion of the output. A double line on the left indicates that the function is "exclusive."

A [http://en.wikipedia.org/wiki/Truth_table truth table] is a useful way to describe the behavior or function of a logic gate. Logic states for both inputs and outputs are designated with 0's and 1's. Click [[Logic Gates: Symbols and Truth Tables|here]] to see some common logic gates and their truth tables.

Logic gates can be placed in parallel or serial combinations in order to perform more complex functions. Here, two parallel outputs, D and E, act as inputs to the function of output Q:

Figure 1. Combination of logic gates: Q = D OR E = (NOT(A OR B)) OR (B AND C)

[[Image:combination_gates.gif]]

([http://www.kpsec.freeuk.com/gates.htm Image] by John Hewes, 2007)

=Logic Gates in Synthetic Biology=
Synthetic biology applies many of the principles of engineering to the field of biology in order to create biological devices which can ultimately be integrated into increasingly complex systems. These principles include standardization of parts, modularity, abstraction, reliability, predictability, and uniformity (Adrianantoandro ''et al.'', 2006). The application of engineering principles to biology is complicated by the inability to predict the functions of even simple devices and modules within the cellular environment. Some of the confounding factors are [[Term paper wiki|gene expression noise]], mutation, cell death, undefined and changing extracellular environments, and interactions with the cellular context (Adrianantoandro et al., 2006).

While for digital logic, inputs are either on or off (1 or 0), biological logic is sometimes leads to intermediate induction levels (Voigt, 2006). However, due to their [http://en.wikipedia.org/wiki/Sigmoid_function sigmoid-shaped] dose response curves, gene regulation systems can be considered genetic analog-digital converters. The signal is either ON or OFF for a wide range of input concentrations, with the system changing between the ON and OFF states in a relatively small concentration window (Kramer et al., 2004).

In synthetic biology, logic gates are created by engineering the biochemical reactions that regulate various cellular processes, such as transcription, translation, protein phosphorylation, allosteric regulation, ligand/receptor binding, and enzymatic reactions. Although the diversity of biochemical reactions can make it difficult to combine different devices, these logic gates can be used to build complex systems with functions that have many practical applications. [http://gcat.davidson.edu/GcatWiki/index.php/CellularMemory:Mathematical_Models Mathematical modeling] is used to predict the dynamics of the signaling and regulatory networks resulting from the logic gates.

=Biomolecular Logic Gates:'' In Vitro''=

''In vitro'' studies have been used to design combinations of molecules that have emergent properties related to information processing--molecular computing devices. Both the inputs and outputs consist of molecular species, with the output being a biologically active molecule. The extent to which these devices will be used with the cellular context is unclear--however, they are bound to inspire new directions for research in synthetic biology, and have potential applications in biochemical sensing, pathway engineering, and medical diagnosis and treatment.

==Protein-based Logic Gates==

Enzymes:
*Two coupled enzymes perform in parallel the 'AND' and 'InhibAND' logic gate operations. (Baron et al., 2006) Summary:
*Logic Gates and Elementary Computing by Enzymes. (Baron et al., 2006) Summary:

Synthetic Peptide Networks:
*Boolean Logic Functions of a Synthetic Peptide Network. (Ashkenasy and Ghadiri, 2004) Summary:

Signaling Proteins:
*Engineering synthetic signaling proteins with ultrasensitive input/output control. (Dueber et al., 2007) Summary: Many eukaryotic signaling proteins have natural, modular "input" and "output" domains: the "inputs" participate in steric or conformational autoinhibitory reactions, and the "outputs" are catalytically, constitutively active domains. Simple synthetic switch functions can be engineered by swapping the regulatory and catalytic domains. This paper describes the engineering of "ultrasensitive switches" for use in more complex regulatory networks, by combining multiple identical modular autoinhibitory domains that function [http://en.wikipedia.org/wiki/Cooperativity cooperatively]. Mathematical models are used to simulate the behavior of these multivalent domain switches and explore the effect of autoinhibitory interaction number and affinity: an external input ligand alters the population distribution of active vs. inactive enzymes, where individual states are "fully repressed in the presence of any intramolecular interactions and fully active only in the absence of all intramolecular interactions" (661). The predictions from the models are tested using synthetic switches built by linking the catalytic output domain of the protein N-WASP to novel peptide input, demonstrating that it is possible to engineer nonlinear switches based on the cooperativity of simple autoinhibitory components. Other complex switches, such as ones that integrate three input signals, have also been built using this approach.

==Deoxyribozyme-based Logic Gates==

Deoxyribozyme-based gates are controlled by oligonucleotide inputs, and have been used to engineer logic gates that perform:
*multiple logical operations in parallel
*single-step signaling cascades
*a feedback cycle that acts as an exponential chain reaction

These papers provide examples of deoxyribozyme-based logic gates:
*Deoxyribozyme-Based Ligase Logic Gates and Their Initial Circuits. (Stojanovic et al., 2005) Summary:
*Deoxyribozyme-Based Three-Input Logic Gates and Construction of a Molecular Full Adder. (Lederman et al., 2006) Summary:
*Construction of Molecular Logic Gates with a DNA-Cleaving Deoxyribozyme (Chen et al., 2006) Summary:

==DNA-based Logic Gates==

*Modular Multi-Level Circuits from Immobilized DNA-Based Logic Gates. (Frezza et al., 2007) Summary:
*DNA Logic Gates Based on Structural Polymorphism of Telomere DNA Molecules Responding to Chemical Input Signals. (Miyoshi et al., 2006) Summary:
*Photonic boolean logic gates based on DNA aptamers. (Yoshida and Yokobayashi, 2006) Summary:
*Parallel Molecular Computations of Pairwise Exclusive-OR (XOR) Using DNA "String Tile" Self-Assembly. (Yan et al., 2003) Summary:

==An autonomous molecular computer for logical control of gene expression. (Benenson et al., 2004)==

===Overview:===

This gate uses sequence recognition to control enzyme catalysis of covalent bond formation and breakage, producing an ssDNA output.

*Input: Specific combination mRNA levels, serving as a simple model of a disease state (cancer)
*Output: “Drug” (or drug repressor) in the form of a single-stranded DNA sequence with known anti-cancer activity

===Design:===

[[Figure 2. Logical design of the molecular computer.]]

An ''in vitro'' system is designed to recognize a specific combination of mRNA levels as its inputs, and performs a logical operation that results in the production of a molecule that can affect gene expression. The input mRNA levels have been designed to mimic a simplistic version of gene expression modeling cancer, and the output is a drug-like ssDNA with known anticancer activity. Therefore, the molecular computer is analogous to "a computational version of 'diagnosis'" (424).

===Function:===

[[Figure 3. Operation of the molecular computer.]]

The molecular computers consist of a double-stranded DNA sequence with unique 7-bp sequences that recognize the “input” RNA. Each mRNA indicator is processed one at a time. An “inactivation tag” results in the displacement of the transition molecule, which destroys the computation fragment. An “activation tag” results in activation of the transition molecule. There are two separate molecular computers working simultaneously—one that releases the drug (a specific single-stranded DNA sequence) upon “positive diagnosis,” and another that releases the drug suppressor in response to “negative diagnosis.” The drug (or drug repressor) is incorporated into the DNA fragment as an inactive loop, protected by the double-stranded recognition sequences. If all of the transitions are “positive,” the ultimate output is drug administration (release of ssDNA). If any of the steps results in a “negative” transition, the output is a drug repressor. The design is flexible in that any sufficiently long mRNA molecule can be used as an indicator, and any ssDNA or short RNA molecule (up to at least 21-bp) can be designed as the output. PCR was used to for the experimental demonstration of both diagnosis and drug administration, and it is shown that the amount of active drug increases with the confidence of positive diagnosis. Future work could test the effectiveness of using alternate inputs (such as proteins), alternate outputs (such as RNA interference), as well as testing the function of the molecular computer ''in vivo''.

==Enzyme-Free Nucleic Acid Logic Circuits. (Seelig et al., 2006)==

Overview:

*Input: 2 DNA strands - F(in) and G(in)
*Gate: 3 DNA strands - E(q), F(f), and G
*Output: Fluorescence - release of F(f)

Nucleic acid devices are simplified by the predicability of base pairing. Although previous research has engineered nucleic acid logic switches based on hybridization and conformational changes ''in vivo'', and this paper designs chemical logic gates that are capable of being combined into large, reliable circuits. These logic gates embody the following digital design principles: logic, cascading, restoration, fan-out, and modularity.

The benefits of this approach are that both inputs and outputs are in the same form, which makes cascading possible (the output for one gate serves as the input for the next gate in the circuit).

Gate function is entirely determined by base pairing and breaking. Each gate is composed of one or more gate strands, and one output strand.

[[Figure 4. Two-input AND gate.]]

Restoration:
*When a gate fails to produce enough output when triggered, restoration increases a moderate output amount to the full activation level.
*When a gate "leaks" by spontaneously releasing the output strand, restoration decreases the small output amount to a negligible level.
*Gates for [[Restoration: Amplification Gate|amplification]] and [[Restoration: Thresholding Gate|thresholding]] were used to implement signal restoration.

Modularity and Scalability:
*Eleven gates (AND, OR, sequence translation, input amplification, and signal restoration) were used to compose a large, complex circuit.

=Cellular Logic Gates: ''In Vivo''=

As stated above, there the cellular environment possesses many characteristics that complicate the implementation of biomolecular logic circuits, such as gene expression noise, mutation, cell death, undefined and changing extracellular environments, and interactions with the cellular context. Some researchers are trying to engineer the "minimal cell," either through top-down or bottom-up approaches, in order to produce a living unit that can perform logical operations without some of these excess confounding factors found in the cells used . These efforts were recently reviewed by [http://www.nature.com/msb/journal/v2/n1/pdf/msb4100090.pdf Forster and Church (2006)]. However, many efforts at building cellular logic gates have succeeded, both in prokaryotes and eukaryotes, with wide-ranging applications. Below are summaries of a few examples.

==Synthetic Oscillators and Switches==
[[Image:design_fig_1ab.gif|Synthetic oscillators and switches.]]

[[Image:design_fig_1d.gif]]

(Image from Figure 1 of Drubin et al., 2007. Permission pending.)

Figure 5. Schematic representation of the function of various engineered biological switches.

A. [[Repressilator]] (Elowitz and Leibler, 2000). The "repressilator," or synthetic cellular oscillator, can be built from a string of three repressors, each acting the repress the expression of the next gene in the circuit. Oscillatory output is read by GFP expression regulated by one of the repressors (in this case, tetracycline). The design is analogous to a series of three NOT gates.

B. Toggle switch (Gardner et al., 2000). A toggle switch can be designed from two repressor systems that cross-regulate each other's promoters, and is analogous to the construction of two independent IF gates. It is useful as a pathway module to create more complex programmable cells. Toggle switches can be used to engineer [[CellularMemory:Toggle_Switch|cellular memory]].

D. RNA-based antiswitch. When the engineered RNA is bound to an inducer ligand, the structure changes to either expose or hide a region of RNA that is homologous to a region of the target mRNA (encompassing the translational start site). Thus, when the antisense region is exposed, translation of target mRNA is repressed. Synthetic switches in both eukaryotes ([[Antiswitches|antiswitches]]) and prokaryotes ([[Riboregulators|riboregulators]] and [[Riboswitches|riboswitches]]) can be mediated via RNA devices.

==Environmental signal integration by a modular AND gate. (Anderson et al., 2007)==

AND gates allow cells to integrate multiple signals and can increase their specificity in sensing the environment. This is especially useful for engineering cells to sense environments that are not encountered naturally, or those that are too specific to be identified by a single environmental signal.

A logic gate achieves modularity when it can be used as a self-contained component of more complex systems, and is designed to interface with multiple different inputs and outputs. This paper demonstrates a modular AND gate in ''E. coli'' that uses promoters for both the inputs and the output. This design, unlike that of previously engineered prokaryotic logic gates, makes it relatively easy to use the same gate for different inputs and outputs.

Overview:
*Inputs:
# Promoter that drives the expression of the gene for T7 RNA polymerase (''T7ptag''), containing two [http://en.wikipedia.org/wiki/Stop_codon amber stop codons] that prevent translation under normal circumstances.
#Promoter that drives the expression of the gene for the SupD amber suppressor (''supD''), which decodes the amber stop codons as serine, and allows for translation of T7 RNA polymerase.
*Output: Gene expressed under the T7 promoter, which requires functional translation of both T7 RNA polymerase and the SupD amber suppressor.

The fluorescence data (see [Figure 2. Function of the AND gate. Figure 2]) was used to parameterize a [[transfer function model]] that was derived in order to understand how the range of the input promoters affects the function of the circuit.

[[Figure 1. Schematic representation of AND gate.]]

*Input 1: P(sal) controls the expression supD gene, and is activated by the addition of salicylate (Sal).
*Input 2: P(BAD) controls T7 RNA polymerase gene, and is activated by the addition of arabinose (Ara). Because a strong RBS for T7ptag resulted in high levels of basal expression, the RBS was mutagenized and tuned by screening for the presence of output only when both inducers were present.
*Output: ''GFPmut3_LAA'' (fast-folding GFP with a degradation tag)

[[Figure 2. Function of the AND gate.]]

There is 1000-fold induction of fluorescence in the presence of high concentrations of both inducers, arabinose and salicylate.

[[Figure 6. Modularity of the AND gate.]]

The modularity of the AND gate is demonstrated by reconnecting the gate to new inputs (natural promoters), and a new output (desired phenotype).

A. Exchanging the inputs:
*Input 1: The ''lux'' promoter, which responds to the [[Quorum Sensing|quorum signal]] AI-1.
*Input 2: The ''mgrB'' promoter, which responds to the absence of exogenous magnesium via the PhoPQ two-component system.

B. Exchanging the output:
*Output: The ''inv'' gene, coding for invasin, a protein that allows bacteria to invade mammalian cells.

==BioLogic Gates Enable Logical Transcription Control in Mammalian Cells. (Kramer et al., 2004)==

Transcription control modules, responsive to up to three small molecule inputs, were engineered in mammalian Chinese hamster ovary cells. These "BioLogic gates" provide the tools and building blocks to engineer more complex gene regulatory networks in eukaryotic cells. The gates use butyrolactone-, streptogramin-, tetracycline-, and macrolide-dependent transcription factors, each fused to a KRAB or VP16 repression domain.

#[[NOT IF gate]] - two inputs
#[[NOT IF IF gate]] - three inputs
#[[NAND gate]] - parallel arrangement of two NOT gates
#[[OR gate]] - parallel arrangement of two IF gates
#[[NOR gate]] - constructed from two NOT gates in consecutive order
#[[INVERTER gate]] - combination of two independent IF gates, acts as the inverse of the NOT IF gate

==Cellular Logic with Orthogonal Ribosomes. (Rackham and Chin, 2006)==

Overview:

Two-input logic gates were constructed based on the interactions between synthetic O-rRNA and O-mRNA.
*Input: Orthogonal ribosomes (O-ribosomes), which translate O-mRNA, but do not significantly translate any of the thousands of cellular transcripts bearing cellular RBS sequences.
*Gate: Orthogonal mRNAs (O-mRNAs), which contain ribosome-binding sequences (RBSs) that do not direct the translation of downstream genes by endogenous ribosomes.
*Output: Fluorescence.

Three distinct O-ribosome-O-mRNA pairs were isolated, with molecular specificities for independent function. This was accomplished through [[Hunter Stone - Synthetic Biology Seminar|directed evolution]] in two steps:
#A library of new potential RBSs were placed upstream of a novel fusion of the genes encoding chloramphenicol acetyltransferase and uracil phosphoribosyltransferase, and screened against 5-fluorouracil to select for the O-mRNA sequences that were not translated by endogenous ribosomes.
#The selected O-mRNAs were combined with a library of mutated 16s rRNA sequences, and these cells were grown in the presence of chloramphenicol to screen for those in which the mutant ribosomes translated the O-mRNAs.
The O-ribosome-O-mRNA pairs can be used to control almost any molecular interaction that can be linked to gene expression. In this case they were used to build an AND gate composed of two O-mRNA sequences: O-mRNA-A-omega, encoding the omega fragment of beta-galactosidase, and O-mRNA-C-alpha, encoding the alpha fragment of beta-galactosidase. Synthesis and assembly of a complete beta-galactosidase enzyme (both fragments) results in the cells hydrolyzing FDG into F (fluorescein), which is detected with a fluorometer. Cells programmed with both of the corresponding O-ribosome inputs, O-rRNA-A and O-rRNA-C, exhibited a 20-fold increase in fluorescence when compared with cells containing any other rRNA combinations.

[[Figure 1. O-ribosomes and Boolean logic.]]

=Applications and Future Directions=

Logic gates can be used to design increasingly complex circuits with far-reaching applications in:
*Genetic engineering
*Nanotechnology
*Industrial Fermentation
*Metabolic engineering:
**increasingly complex synthetic gene circuits might be used to engineer and optimize novel metabolic pathways.
*[[Medical Applications of Synthetic Biology - Samantha Simpson|Medicine]]:
**Bacteria to deliver cancer treatment: the integration of multiple inputs can help bacteria sense and respond to increasingly specific environments, such as that of a tumor in the human body.
**Pharmaceuticals: produced through metabolic engineering; "smart" drug delivery.

=References=
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*Elowitz, M. B., & Leibler, S. (2000). A synthetic oscillatory network of transcriptional regulators. Nature. 403(6767):335-8. [http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&Cmd=ShowDetailView&TermToSearch=10659856&ordinalpos=10&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVDocSum Abstract]
*Farfel, J., Stefanovic, D. (2005). Towards practical biomolecular computers using microfluidic deoxyribozyme logic gate networks. University of New Mexico.
*Forster, A.C., Church G.M. (2006). Towards synthesis of a minimal cell. Mol Sys Biol. 2(45):1-10. [http://www.nature.com/msb/journal/v2/n1/pdf/msb4100090.pdf PDF]
*Frezza, B.M., Cockroft, S. L., Ghadiri, M.R. (2007). Modular Multi-level Circuits from Immobilized DNA-Based Logic Gates. J Am Chem Soc. (Epub ahead of print) [http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&Cmd=ShowDetailView&TermToSearch=17994734&ordinalpos=2&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVDocSum Abstract]
*Gardner, T.S., Cantor, C.R., Collins, J.J. (2000). Construction of a genetic toggle switch in Escherichia coli. Nature. 403(6767):338-42. [http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&Cmd=ShowDetailView&TermToSearch=10659857&ordinalpos=3&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVDocSum Abstract]
*Heinemann, M., Panke, S. (2006). Synthetic biology—putting engineering into biology. Bioinformatics. 22(22):2790-9. [http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&Cmd=ShowDetailView&TermToSearch=16954140&ordinalpos=2&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVDocSum Abstract]
*Kaznessis, Y. N. (2007). Models for synthetic biology. BMC Syst Biol. 1(1):47. [http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&Cmd=ShowDetailView&TermToSearch=17986347&ordinalpos=1&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVDocSum Abstract]
*Kramer, B. P., Fischer, C., Fussenegger, M. (2004). BioLogic Gates Enable Transcription Control in Mammalian Cells. Biotechnol Bioeng. 87(4):478-84. [http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&Cmd=ShowDetailView&TermToSearch=15286985&ordinalpos=2&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVDocSum Abstract]
*Lederman, H., Macdonald, J., Stefanovic, D., Stojanovic, M. N. (2006). Deoxyribozyme-based three-input logic gates and construction of a molecular full adder. Biochemistry. 45(4):1194-9. [http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&Cmd=ShowDetailView&TermToSearch=16430215&ordinalpos=3&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVDocSum Abstract]
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Ribozyme vesicles

2007-12-11T16:25:54Z

WikiSysop: /* Experimental Design */

== Goals ==
*Create the "simplest possible protocell" capable of having a self-replicating informational molecule and a mechanism for spatial localization such as compartmentalization (Chen ''et al''., 2005).
*Use membrane boundary that can grow and divide with being too complex and that can allow passive diffusion of ion and substrates
*Encapsulation of catalytic (self-replicating) RNA molecules within self-replicating membrane vesicles.

== Experimental Design ==
A unique and beneficial aspect of fatty acid vesicles is that they have autocatalytic growth and can repeatedly divide on their own. The first issue addressed is to create membranes that are stable but can allow passive diffusion of ions and substrates in and out of the vesicle. The reason that this aspect of the protocell is so essential is because the formation of RNA catalysts requires the addition of magnesium ions to create the tertiary structure of the ribozyme. To accomplish this goal, researchers observed the effects of magnesium on the stability and permeability of vesicles consisting of fatty acids known as myristoleic acid (MA) and glycerol monomyristoleate (GMM). Thus, they experimented with different ratios of MA to GMM to increase tolerance of Mg2+ in vesicles and allow for passive diffusion.

{| border="1"
|+ Table 1. MgCl2 Tolerance of Simple Vesicles
! MA:GMM ratio !! [MgCl2] tolerated, assayed by dye leakage (mM) !! [MgCl2] at turbidity change (mM)
|-
| 1:0 || 0.5 || 1
|-
| 4:1 || 2 || 3
|-
| 2:1 || 4 || 6
|}

Table 1. To test the stability of various composititons of MA and GGM, investigators monitored dye retention in the vesicle <1 h after addition of MgCl2. The concentration of MgCl2 that caused leakage to occur is defined as the maximum concentrated tolerated by the vesicle. An additional measure of the maximum concentration of MgCl2 allowed by the vesicle is using the [http://en.wikipedia.org/wiki/Turbidity turbidity] to access the cloudiness created by individual particles.
''Table 1 was re-created using data from Chen ''et al.'', 2005.''

The stability in the presence of Mg2+ was shown to increase as the proportion of GMM increased. However, higher proportions than 2:1 MA to GMM resulted in "the appearance of oil droplets mixed with vesicles" (Chen ''et al.'', 2005). Then, researchers were interested in testing the effects of Mg2+ on the permeability of the vesicles. First, they needed to address whether Mg2+ caused permanent permeability in vesicles. Therefore, they measured the percent of dye leakage of vesicles over time. Dye leakage was found to increase over time in a period of one day, showing that permeability of the vesicle exists permanently throughout the experiment (Figure 1A and 1B). Then, researchers tested whether "large-scale destabilization" occurs in vesicles due to Mg2+ by measuring presence of RNA decamers tagged with fluroescent labels (Chen et al. 2005). They would expect if destabilization occurs then the RNA would leak out of the vesicles but instead they found that RNA remained in the vesicles (Figure 1C). However, a mononucleotide (H-UMP) of RNA was found to be permeable in the same conditions (Figure 1D). The paper attributes this difference between the permeability of mononucleotide of RNA and larger RNA molecule to neutralization of negative charges in the RNA and stabilization caused by Mg2+ of the membrane and solute interactions, which would prevent RNA molecules from leaking. Another reason not mentioned in the paper could be that larger RNA molecules may be too large to efficiently diffuse out of the vesicles whereas smaller RNA mononucleotides may be able to pass through the semi-permeable membrane.

http://pubs.acs.org/isubscribe/journals/jacsat/127/i38/figures/ja051784pf00001.gif

Figure 1. (A) Leakage of encapsulated calcein, a fluorescent dye, was measured over time with or without 4 mM MgCl2, represented by the blue and black lines, respectively. (B) Fractions of encapsulated versus free calcein that has leaked out of the vesicle at 22 hr. (C) Leakage of encapsulated RNA decamer is shown by the difference between encapsulated and free RNA using size-exclusion chromatography after 19 hr. The red line represents response to 4 mM Mg2+ versus the control without Mg2+ (black line). (D) Leakage of encapsulated H-UMP vesicles was measured over time in response to MgCl2 (red) versus the control (black) without MgCl2. ''Image Permission Granted by Jack Szostak.''

In addition, investigators used similar processes by using a fluorescent dye sensitive to magnesium known as magfura-2 to verify that these vesicles were indeed permeable to magnesium.

Lastly, researchers attempted to increase vesicle growth by addition of [http://en.wikipedia.org/wiki/Micelle micelles] to vesicles. It resulted in a ~50% growth in the surface area of the vesicle. Additionally, dodecane is added as a hydrophobic spacer, resulting in 2:1:0: MA:GMM:dodecane micelles. Thus the overall growth of these micelles to vesicles of the same composition was 40% in one equivalent of micelle.

http://upload.wikimedia.org/wikipedia/commons/thumb/c/c6/Phospholipids_aqueous_solution_structures.svg/250px-Phospholipids_aqueous_solution_structures.svg.png

[http://upload.wikimedia.org/wikipedia/commons/thumb/c/c6/Phospholipids_aqueous_solution_structures.svg/250px-Phospholipids_aqueous_solution_structures.svg.png Image Source]

== Results ==
'''"Ribozyme Activity in Simple Vesicles"(Chen et al., 2005)'''

Vesicles of 2:1:0.3 MA:GMM:dodecane were created to encapulate self-cleaving hammerhead ribozymes. This ribozyme (N15min7) is important because it can both cleave and ligate RNA, which will be very important for simple cell-like structures. When Mg2+ is added, the ribozyme cleaves itself into two smaller fragments. The fraction of ribozymes cleaved over time when exposed to 4 mM MgCl2 increased to about 0.66 in unencapsulated vesicles (Figure 2A) and 0.60 in encapsulated vesicles (Figure 2B). The top band on the gel represent the uncleaved ribozymes, while the bottom band represents the cleaved ribozyme, and the lanes correspond with each time point. As the fraction of uncleaved ribozymes decreases, the fraction of cleaved ribozymes increases, which is what we would expect. The vesicles were very stable because even after 15 minutes of exposure to MgCl2, the vesicles remained encapsulated (Figure 2C).

http://pubs.acs.org/isubscribe/journals/jacsat/127/i38/figures/ja051784pf00004.gif

Figure 2. (A and B) The self-cleavage activity of ribozyme N15min7 measured by the fraction cleaved over time. The insets on the graph are phoshorimages of the assay gels. (A) represents unencapsulated ribozymes while (B) represents encapsulated MA:GMM:dodecane ribozymes. (C) Size-exclusion chromatography of MA:GMM:dodecane vesicles of "radiolabeled N15min7 RNA remained encapsulated 15 min after ther addition of MgCl2" (Chen et al., 2005). ''Image Permission Granted by Jack Szostak.''

== Conclusions and Further Experiments ==
Therefore, these researchers sucessfully created vesicles that are permeable to ions and substrates necessary for proper ribozyme function and showed that catalytic ribozyme activity can occur inside these vesicles without any significant loss of functionality. These novel cell-like vesicles open the doors to exploring new ways of engineering and understanding biological systems.

<hr>

== '''[http://gcat.davidson.edu/GcatWiki/index.php/Applications_of_Ribozymes_in_Synthetic_Systems_-_Danielle_Jordan Main Page]''' ==

Ribozyme vesicles

2007-12-11T16:23:22Z

WikiSysop: /* Experimental Design */

== Goals ==
*Create the "simplest possible protocell" capable of having a self-replicating informational molecule and a mechanism for spatial localization such as compartmentalization (Chen ''et al''., 2005).
*Use membrane boundary that can grow and divide with being too complex and that can allow passive diffusion of ion and substrates
*Encapsulation of catalytic (self-replicating) RNA molecules within self-replicating membrane vesicles.

== Experimental Design ==
A unique and beneficial aspect of fatty acid vesicles is that they have autocatalytic growth and can repeatedly divide on their own. The first issue addressed is to create membranes that are stable but can allow passive diffusion of ions and substrates in and out of the vesicle. The reason that this aspect of the protocell is so essential is because the formation of RNA catalysts requires the addition of magnesium ions to create the tertiary structure of the ribozyme. To accomplish this goal, researchers observed the effects of magnesium on the stability and permeability of vesicles consisting of fatty acids known as myristoleic acid (MA) and glycerol monomyristoleate (GMM). Thus, they experimented with different ratios of MA to GMM to increase tolerance of Mg2+ in vesicles and allow for passive diffusion.

{| border="1"
|+ Table 1. MgCl2 Tolerance of Simple Vesicles
! MA:GMM ratio !! [MgCl2] tolerated, assayed by dye leakage (mM) !! [MgCl2] at turbidity change (mM)
|-
| 1:0 || 0.5 || 1
|-
| 4:1 || 2 || 3
|-
| 2:1 || 4 || 6
|}

Table 1. To test the stability of various composititons of MA and GGM, investigators monitored dye retention in the vesicle <1 h after addition of MgCl2. The concentration of MgCl2 that caused leakage to occur is defined as the maximum concentrated tolerated by the vesicle. An additional measure of the maximum concentration of MgCl2 allowed by the vesicle is using the [http://en.wikipedia.org/wiki/Turbidity turbidity] to access the cloudiness created by individual particles.
''Table 1 was re-created using data from Chen ''et al.'', 2005.''

The stability in the presence of Mg2+ was shown to increase as the proportion of GMM increased. However, higher proportions than 2:1 MA to GMM resulted in "the appearance of oil droplets mixed with vesicles" (Chen ''et al.'', 2005). Then, researchers were interested in testing the effects of Mg2+ on the permeability of the vesicles. First, they needed to address whether Mg2+ caused permanent permeability in vesicles. Therefore, they measured the percent of dye leakage of vesicles over time. Dye leakage was found to increase over time in a period of one day, showing that permeability of the vesicle exists permanently throughout the experiment (Figure 1A and 1B). Then, researchers tested whether "large-scale destabilization" occurs in vesicles due to Mg2+ by measuring presence of RNA decamers tagged with fluroescent labels (Chen et al. 2005). They would expect if destabilization occurs then the RNA would leak out of the vesicles but instead they found that RNA remained in the vesicles (Figure 1C). However, a mononucleotide (H-UMP) of RNA was found to be permeable in the same conditions (Figure 1D). The paper attributes this difference between the permeability of mononucleotide of RNA and larger RNA molecule to neutralization of negative charges in the RNA and stabilization caused by Mg2+ of the membrane and solute interactions, which would prevent RNA molecules from leaking. Another reason not mentioned in the paper could be that larger RNA molecules may be too large to efficiently diffuse of the vesicles whereas smaller RNA mononucleotides may be able to pass through the semi-permeable membrane.

http://pubs.acs.org/isubscribe/journals/jacsat/127/i38/figures/ja051784pf00001.gif

Figure 1. (A) Leakage of encapsulated calcein, a fluorescent dye, was measured over time with or without 4 mM MgCl2, represented by the blue and black lines, respectively. (B) Fractions of encapsulated versus free calcein that has leaked out of the vesicle at 22 hr. (C) Leakage of encapsulated RNA decamer is shown by the difference between encapsulated and free RNA using size-exclusion chromatography after 19 hr. The red line represents response to 4 mM Mg2+ versus the control without Mg2+ (black line). (D) Leakage of encapsulated H-UMP vesicles was measured over time in response to MgCl2 (red) versus the control (black) without MgCl2. ''Image Permission Granted by Jack Szostak.''

In addition, investigators used similar processes by using a fluorescent dye sensitive to magnesium known as magfura-2 to verify that these vesicles were indeed permeable to magnesium.

Lastly, researchers attempted to increase vesicle growth by addition of [http://en.wikipedia.org/wiki/Micelle micelles] to vesicles. It resulted in a ~50% growth in the surface area of the vesicle. Additionally, dodecane is added as a hydrophobic spacer, resulting in 2:1:0: MA:GMM:dodecane micelles. Thus the overall growth of these micelles to vesicles of the same composition was 40% in one equivalent of micelle.

http://upload.wikimedia.org/wikipedia/commons/thumb/c/c6/Phospholipids_aqueous_solution_structures.svg/250px-Phospholipids_aqueous_solution_structures.svg.png

[http://upload.wikimedia.org/wikipedia/commons/thumb/c/c6/Phospholipids_aqueous_solution_structures.svg/250px-Phospholipids_aqueous_solution_structures.svg.png Image Source]

== Results ==
'''"Ribozyme Activity in Simple Vesicles"(Chen et al., 2005)'''

Vesicles of 2:1:0.3 MA:GMM:dodecane were created to encapulate self-cleaving hammerhead ribozymes. This ribozyme (N15min7) is important because it can both cleave and ligate RNA, which will be very important for simple cell-like structures. When Mg2+ is added, the ribozyme cleaves itself into two smaller fragments. The fraction of ribozymes cleaved over time when exposed to 4 mM MgCl2 increased to about 0.66 in unencapsulated vesicles (Figure 2A) and 0.60 in encapsulated vesicles (Figure 2B). The top band on the gel represent the uncleaved ribozymes, while the bottom band represents the cleaved ribozyme, and the lanes correspond with each time point. As the fraction of uncleaved ribozymes decreases, the fraction of cleaved ribozymes increases, which is what we would expect. The vesicles were very stable because even after 15 minutes of exposure to MgCl2, the vesicles remained encapsulated (Figure 2C).

http://pubs.acs.org/isubscribe/journals/jacsat/127/i38/figures/ja051784pf00004.gif

Figure 2. (A and B) The self-cleavage activity of ribozyme N15min7 measured by the fraction cleaved over time. The insets on the graph are phoshorimages of the assay gels. (A) represents unencapsulated ribozymes while (B) represents encapsulated MA:GMM:dodecane ribozymes. (C) Size-exclusion chromatography of MA:GMM:dodecane vesicles of "radiolabeled N15min7 RNA remained encapsulated 15 min after ther addition of MgCl2" (Chen et al., 2005). ''Image Permission Granted by Jack Szostak.''

== Conclusions and Further Experiments ==
Therefore, these researchers sucessfully created vesicles that are permeable to ions and substrates necessary for proper ribozyme function and showed that catalytic ribozyme activity can occur inside these vesicles without any significant loss of functionality. These novel cell-like vesicles open the doors to exploring new ways of engineering and understanding biological systems.

<hr>

== '''[http://gcat.davidson.edu/GcatWiki/index.php/Applications_of_Ribozymes_in_Synthetic_Systems_-_Danielle_Jordan Main Page]''' ==

Ribozyme vesicles

2007-12-11T16:20:34Z

WikiSysop: /* Goals */

== Goals ==
*Create the "simplest possible protocell" capable of having a self-replicating informational molecule and a mechanism for spatial localization such as compartmentalization (Chen ''et al''., 2005).
*Use membrane boundary that can grow and divide with being too complex and that can allow passive diffusion of ion and substrates
*Encapsulation of catalytic (self-replicating) RNA molecules within self-replicating membrane vesicles.

== Experimental Design ==
A unique and beneficial aspect of fatty acid vesicles is that they have autocatalytic growth and can repeatedly divide on their own. The first issue addressed is to create membranes that are stable but can allow passive diffusion of ions and substrates in and out of the vesicle. The reason that this aspect of the protocell is so essential is because the formation of RNA catalysts requires the addition of magnesium ions to create the tertiary structure of the ribozyme. To accomplish this goal, researchers observed the effects of magnesium on the stability and permeability of vesicles consisting of fatty acids known as myristoleic acid (MA) and glycerol monomyristoleate (GMM). Thus, they experimented with different ratios of MA to GMM to increase tolerance of Mg2+ in vesicles and allow for passive diffusion.

{| border="1"
|+ Table 1. MgCl2 Tolerance of Simple Vesicles
! MA:GMM ratio !! [MgCl2] tolerated, assayed by dye leakage (mM) !! [MgCl2] at turbidity change (mM)
|-
| 1:0 || 0.5 || 1
|-
| 4:1 || 2 || 3
|-
| 2:1 || 4 || 6
|}

Table 1. To test the stability of various composititons of MA and GGM, investigators monitored dye retention in the vesicle <1 h after addition of MgCl2. The concentration of MgCl2 that caused leakage to occur is defined as the maximum concentrated tolerated by the vesicle. An additional measure of the maximum concentration of MgCl2 allowed by the vesicle is using the [http://en.wikipedia.org/wiki/Turbidity turbidity] to access the cloudiness created by individual particles.
''Table 1 was re-created using data from Chen et al. 2005.''

The stability in the presence of Mg2+ was shown to increase as the proportion of GMM increased. However, higher proportions than 2:1 MA to GMM resulted in "the appearance of oil droplets mixed with vesicles" (Chen et al. 2005). Then, researchers were interested in testing the effects of Mg2+ on the permeability of the vesicles. First, they needed to address whether Mg2+ caused permanent permeability in vesicles. Therefore, they measured the percent of dye leakage of vesicles over time. Dye leakage was found to increase over time in a period of one day, showing that permeability of the vesicle exists permanently throughout the experiment (Figure 1A and 1B). Then, researchers tested whether "large-scale destabilization" occurs in vesicles due to Mg2+ by measuring presence of RNA decamers tagged with fluroescent labels (Chen et al. 2005). They would expect if destabilization occurs then the RNA would leak out of the vesicles but instead they found that RNA remained in the vesicles (Figure 1C). However, a mononucleotide (H-UMP) of RNA was found to be permeable in the same conditions (Figure 1D). The paper attributes this difference between the permeability of mononucleotide of RNA and larger RNA molecule to neutralization of negative charges in the RNA and stabilization caused by Mg2+ of the membrane and solute interactions, which would prevent RNA molecules from leaking. Another reason not mentioned in the paper could be that larger RNA molecules may be too large to efficiently diffuse of the vesicles whereas smaller RNA mononucleotides may be able to pass through the semi-permeable membrane.

http://pubs.acs.org/isubscribe/journals/jacsat/127/i38/figures/ja051784pf00001.gif

Figure 1. (A) Leakage of encapsulated calcein, a fluorescent dye, was measured over time with or without 4 mM MgCl2, represented by the blue and black lines, respectively. (B) Fractions of encapsulated versus free calcein that has leaked out of the vesicle at 22 hr. (C) Leakage of encapsulated RNA decamer is shown by the difference between encapsulated and free RNA using size-exclusion chromatography after 19 hr. The red line represents response to 4 mM Mg2+ versus the control without Mg2+ (black line). (D) Leakage of encapsulated H-UMP vesicles was measured over time in response to MgCl2 (red) versus the control (black) without MgCl2. ''Image Permission Granted by Jack Szostak.''

In addition, investigators used similar processes by using a fluorescent dye sensitive to magnesium known as magfura-2 to verify that these vesicles were indeed permeable to magnesium.

Lastly, researchers attempted to increase vesicle growth by addition of [http://en.wikipedia.org/wiki/Micelle micelles] to vesicles. It resulted in a ~50% growth in the surface area of the vesicle. Additionally, dodecane is added as a hydrophobic spacer, resulting in 2:1:0: MA:GMM:dodecane micelles. Thus the overall growth of these micelles to vesicles of the same composition was 40% in one equivalent of micelle.

http://upload.wikimedia.org/wikipedia/commons/thumb/c/c6/Phospholipids_aqueous_solution_structures.svg/250px-Phospholipids_aqueous_solution_structures.svg.png

[http://upload.wikimedia.org/wikipedia/commons/thumb/c/c6/Phospholipids_aqueous_solution_structures.svg/250px-Phospholipids_aqueous_solution_structures.svg.png Image Source]

== Results ==
'''"Ribozyme Activity in Simple Vesicles"(Chen et al., 2005)'''

Vesicles of 2:1:0.3 MA:GMM:dodecane were created to encapulate self-cleaving hammerhead ribozymes. This ribozyme (N15min7) is important because it can both cleave and ligate RNA, which will be very important for simple cell-like structures. When Mg2+ is added, the ribozyme cleaves itself into two smaller fragments. The fraction of ribozymes cleaved over time when exposed to 4 mM MgCl2 increased to about 0.66 in unencapsulated vesicles (Figure 2A) and 0.60 in encapsulated vesicles (Figure 2B). The top band on the gel represent the uncleaved ribozymes, while the bottom band represents the cleaved ribozyme, and the lanes correspond with each time point. As the fraction of uncleaved ribozymes decreases, the fraction of cleaved ribozymes increases, which is what we would expect. The vesicles were very stable because even after 15 minutes of exposure to MgCl2, the vesicles remained encapsulated (Figure 2C).

http://pubs.acs.org/isubscribe/journals/jacsat/127/i38/figures/ja051784pf00004.gif

Figure 2. (A and B) The self-cleavage activity of ribozyme N15min7 measured by the fraction cleaved over time. The insets on the graph are phoshorimages of the assay gels. (A) represents unencapsulated ribozymes while (B) represents encapsulated MA:GMM:dodecane ribozymes. (C) Size-exclusion chromatography of MA:GMM:dodecane vesicles of "radiolabeled N15min7 RNA remained encapsulated 15 min after ther addition of MgCl2" (Chen et al., 2005). ''Image Permission Granted by Jack Szostak.''

== Conclusions and Further Experiments ==
Therefore, these researchers sucessfully created vesicles that are permeable to ions and substrates necessary for proper ribozyme function and showed that catalytic ribozyme activity can occur inside these vesicles without any significant loss of functionality. These novel cell-like vesicles open the doors to exploring new ways of engineering and understanding biological systems.

<hr>

== '''[http://gcat.davidson.edu/GcatWiki/index.php/Applications_of_Ribozymes_in_Synthetic_Systems_-_Danielle_Jordan Main Page]''' ==

Post-transcriptional Regulation Technologies - Erin Zwack

2007-12-11T15:47:45Z

WikiSysop: /* Use of Post-transciptional Regulatory Technologies */

==Post-transcriptional Regulation Technologies==

==Overview:==

Regulation of translation provides an excellent tool for research on metabolic and other pathways in organisms, and for the production of different sensors by controlling the translation of specific genes depending on cellular conditions. Further development of these types of technologies could provide a “knock-down” equivalent to RNAi, which exist in some eukaryotes. A gene of interest could be expressed normally at all times when the regulator is not active; thus, no ill effects will result before a particular pathway activates and produces a specific ligand if the gene has another purpose as well. Other synthetic biologists could use these technologies to engineer fast-responding, RNA-based biological sensors for environmental chemicals, or novel pathways.

Using RNA regulatory molecules instead of regulatory proteins to control gene expression provides several benefits to synthetic biologists. Regulatory proteins bind to specific sites such as sites on the promoter or sites upstream of the promoter called operators ([http://en.wikipedia.org/wiki/Gene_regulation#Regulatory_protein wikipedia]). Control by these proteins can rely heavily on [[CellularMemory:Mathematical Models#Cooperativity_and_Bistability| cooperativity]] (Gardner et al. 2000), that is multiple proteins binding to one site, in order to see an effect. Regulatory RNA molecules on the other hand need a one to one ratio of regulatory molecule to target. As long as the molecule is expressed in an equal or greater amount than the target, the regulatory RNAs will normally be able to bind to their targets and control transcription.

While synthetic biologists could use the regulatory proteins and their binding sites that are found in nature, rational design of more new regulatory proteins is difficult. With the oligo and gene sythesis technology in existence today, RNA can be engineered that complements and thus targets any other RNA sequence. Regulatory proteins are also controlled mainly by promoter when determining whether they are active or not. As only so many inducible promoters exist, there is a small number of stimuli that can be used to determine under what conditions the gene under a regulatory protein's control will be expressed or repressed. An aptamer, RNA sequence that binds to a small molecule such as theophylline (Bayer and Smolke 2005), can be used not only to regulate gene expression but can also regulate under what conditions an RNA regulatory molecule is active. New aptamers are easily developed through rational design, and the number in existence is continually increasing and providing new molecules that can act as ligands.

Finally, regulatory proteins stop gene expression before transcription. When the stimulus changes and the gene is expressed (either because a regulatory protein has now bound to or has released its site), the time it takes for the phenotype to be expressed is longer because both transcription and translation must occur instead of just translation. With RNA, the gene expression is halted after transcription. Once the stimulus is removed, the RNA already produced by the gene simply needs to be translated.

==Development of Systems==

In most cases, post-transcriptional regulatory mechanisms that were developed and worked in eukaryotes cannot be directly transferred to prokaryotes. Modifications are necessary because eukaryotic and prokaryotic transcription and translation do not follow the exact same path. In eukaryotes, mRNA must have introns spliced out before translation begins; thus, any mechanism that regulates translation has time to bind or manipulate the mRNA (figure 1). In prokaryotes, translation begins as soon as the ribosomal binding site (RBS) is transcribed and accessible to a ribosome (figure 2).

[[Image:Eukaryotic.jpg]]

'''Figure 1:''' Inside of the nucleus of the eukaryote, the gene is transcribed into pre-mRNA, which contain both introns (orange) and exons (red). The pre-mRNA is then modified so that the introns are spliced out and the exons are put together. Finally the mRNA are translated by the ribosome in the cytoplasm.

[[Image:Prokaryote.jpg]]

'''Figure 2:''' In a prokaryotic cell the DNA is transcribed by RNA polymerase. As soon as the polymerase transcribes the ribosomal binding site, a ribosome binds and begins translating the sequence into protein. There is no modification step between transcription and translation.

==Eukaryotes:==

[[Antiswitches]]

==Prokaryotes:==

[[Riboregulators]]

[[Riboswitches]]

==Use of Post-transciptional Regulatory Technologies==

Regulation of translation provides an excellent tool for research on metabolic and other pathways in organisms, and for the production of different sensors by controlling the translation of specific genes depending on cellular conditions. Researchers can turn off translation of certain genes in response to different pathways being activated, such as metabolic pathways. If a researcher wanted to know if a particular gene were necessary to proper function of a pathway, the aptamer of the antiswitch or riboswitch could be designed to have a molecule produced upstream in the pathway be its ligand. The gene would be expressed normally at all times when the pathway is not active; thus, no ill effects will result before the pathway activates if the gene has another purpose as well. This would provide a “knock-down” equivalent to RNAi, which exists in some eukaryotes.

Synthetic biologists could engineer fast-responding, RNA-based biological sensors for environmental chemicals. The sensors could be used to detect harmful chemical like arsenic or chemical indicative of explosives by using post-transcriptional regulatory technologies that would activate or be produced in the presence of the chemical and turn-on a reporter gene like GFP. The technology could also be used to engineer novel pathways that only activate when the environmental conditions are favorable. By regulating when a newly engineered pathway is on, biologists could possibly achieve optimal efficiency in generating a desired product by not taxing the cells when they do not have enough resources to thrive and produce a product that is not natural to the organism.

==The Labs==

[http://www.che.caltech.edu/groups/cds/index.htm Smolke]

[http://www.bu.edu/abl/ Collins]

[http://www.bu.edu/abl/files/naturebiotech_isaacs.pdf Dr. Isaacs's Review of RNA Synthetic Biology]

[http://gallivan1.chem.emory.edu/Gallivan%20Lab/Home.html Gallivan]

==References==

[http://www.ncbi.nlm.nih.gov/sites/entrez?cmd=Retrieve&db=pubmed&dopt=AbstractPlus&list_uids=15723047 Bayer TS and Smolke CD. Programmable ligand-controlled riboregulators of eukaryotic gene expression. ''Nat Biotechnol''. (2005) 3:337-43.]

[http://pubs.acs.org/cgi-bin/article.cgi/jacsat/2004/126/i41/pdf/ja048634j.pdf Desai SK and Gallivan JP. Genetic Screens and Selections for Small Molecules Based on a Synthetic Riboswitch That Activates Protein Translation. ''J. Am. Chem. Soc.''(2004) 126:13247-54.]

[http://www.bio.davidson.edu/courses/synthetic/papers/Collins_ToggleSwitch.pdf Gardner, T.S., Cantor, C.R., and Collins, J.J. Construction of a genetic toggle switch in Eschreichia coli. ''Nature'' (2000) 403: 339-342.]

Isaacs FJ, et al. Engineered riboregulators enable post-transcriptional control of gene expression. ''Nat Biotechnol.'' (2004) 22:841-47.[http://www.bio.davidson.edu/Courses/Synthetic/papers/RNA_Regulation.pdf]

Regulatory Proteins. Wikipedia. December 2007. http://en.wikipedia.org/wiki/Gene_regulation#Regulatory_protein

[[A Review of Synthetic Biology| Return to Main Page]]

Riboswitches

2007-12-11T15:45:36Z

WikiSysop: /* From Concept to Wet Lab */

All information on Riboswitches came from Desai and Gallivan (2004).

Riboswitches are small sequences in mRNA that bind small molecules to regulate translation and occasionally transcription. Riboswitches occur naturally in both eukaryotes and prokaryotes. Desai and Gallivan hoped to find new synthetic riboswitches (riboswitches with new ligand specificities) by creating libraries of mutant riboswitches and using genetic selection to pick the functional ones of interest. Desai and Gallivan also employed riboswitches to screen for the presence of specific small molecules. In theory, riboswitches are perfect because the number of aptamers already in existence and our capbaility to engineer new aptamers through rational design provide great versatility in shoosing stimuli and conditions.

==Design==

Reviews of previous research showed that the theophylline aptamer worked in riboswitches in wheat germ, a eukaryote, and ''Bacillus subtilis'', a Gram positive bacterium. Desai and Gallivan decided to translate the technology to Gram negative bacteria. To do this they cloned the theophylline aptamer five base pairs upstream of the RBS for ''lacZ'', a gene that produces the enzyme beta-galactosidase. The gene is controlled by a weak promoter and a weak RBS allowing for sensitivity to changes in translation because of the presence of theophylline. The construct was then transformed into ''E. coli''. When theophylline is added, translation should be turned on again.

==From Concept to Wet Lab==

The aptamer for theophylline was inserted in front of the gene for beta-galactosidase gene to create an mRNA with a riboswitch. Beta-galactosidase activity is dependent on the amount of the enzyme beta-galactosidase; therefore, measurements of beta-galactosidase activity indicate whether translation of the mRNA is taking place. Testing and comparing beta-galactosidase activity for cells with the riboswitch in the presence of theophylline, caffeine, and no ligand as well as testing and comparing beta-galactosidase activity for cells without the riboswitch under the same conditions demonstrated that the riboswitch does control gene expression in response to theophylline (figure 14). Significant increases in beta-galactosidase activity in cells with the riboswitch were only found when theophylline was added. Because beta-galactosidase activity in the presence of theophylline, caffeine, or no ligand for cells without a riboswitch did not significantly vary, the increase in beta-galactosidase activity in the presence of theophylline for cells with a riboswitch most likely resulted from the theophylline binding to the riboswitch and allowing translation.

[[Image:Plate.JPG]][[Image:Beta.JPG]][[Image:Beta_bar.JPG]]

(Desai and Gallivan 2004)

'''Figure 14:''' '''a)''' On the plate are cells with the riboswitch that were grown in three different conditions: theophylline present, caffeine present, and no ligand present. Cells that were grown in the presence of theophylline have a distinct blue/green color, which indicates beta-galactosidase activity, compared to the cells grown with caffeine or without ligand. The lack of color in the cells grown with caffeine or without ligand indicates that the riboswitch most likely prevents translation of the mRNA encoding beta-galactosidase. The blue color of the cells grown with theophylline indicates that theophylline probably is binding to the riboswitch and allowing for translation.''' b)''' The green line represents cells that have had theophylline added to them while the red line represents cells that had caffeine added. Beta-galactosidase activity's increasing, as measured by Miller Units, significantly only in the presence of high enough concentrations of theophylline but not caffeine suggests that the riboswitch is highly specific for its ligand. '''c)''' When no riboswitch existed in the beta-galactosidase transcript, beta-galactosidase activity was not significantly different for cells grown in the presence of theophylline, caffeine, or no ligand. When a riboswitch existed in the beta-galactosidase transcript, cells grown in the presence of theophylline exhibited significantly greater beta-galactosidase activity than cells grown in the presence of caffeine or no ligand. Beta-galactosidase activity for cells grown in the presence of caffeine did not vary significantly from activity for cells grown without any ligand present.

To further support that the theophylline was interacting with the aptamer and not just increasing protein translation through another route, a single point mutation that decreases affinity for theophylline ''in vitro'' and increases affinity for 3-methylxanthine was introduced to the aptamer. When using this mutant riboswitch in the presence of theophylline, the beta-galactosidase activity was almost the same as beta-galactosidase activity without any small molecule while beta-galactosidase in the presence of 3-methylxanthine was significantly higher than base-line. These results suggest that the change in translation as indicated by the increase of beta-galactosidase is controlled by the riboswitch and not some other mechanism (figure 15).

[[Image:Mutation.JPG]]

(Desai and Gallivan 2004)

'''Figure 15:''' Beta-galactosidase activity was low and basically the same for cells with the mutant riboswitch that had no molecule (black), caffeine (red), or theophylline (green) added. Cells with the mutant riboswitch showed significant increase in beta-galactosidase activity when 3-methylxanthine (blue) was added.

In synthetic biology, parts are often tweaked until the efficiency, detectability, and other properties are enhanced to work as a part in a device. Gallivan's lab worked on optimization of the riboswitch by changing the position of the aptamer in relation to the RBS. The riboswitch showed a greater increase in beta-galactosidase activity when the aptamer was 8 base pairs upstream of the RBS than when the aptamer was either 2 or 5 base pairs upstream (figure 16). This result could be because of the surrounding bases being purines in this particular transcript or the actual distance.

[[Image:Optimization.JPG]]

(Desai and Gallivan 2004)

'''Figure 16:''' Cells exposed to theophylline (green) showed greater beta-galactosidase activity than cells exposed to caffeine (red) or cells exposed to nothing (black) no matter the distance of the riboswitch from the RBS, but the greatest increase was experienced by cells with the riboswitch 8 base pairs (the farthest tested distance) from the RBS when exposed to theophylline.

==Modularity==

An advantage of riboswitches is that they are modular. The aptamer that is in front of the RBS does not have the restriction that it only works in front of specific sequences. A riboswitch can technically be integrated into any mRNA without redesigning the whole riboswitch component unlike with antiswitches. Because riboswitches do not have a complement to any specific RBS, they can function in front of RBS's that vary drastically from each other while Isaac's riboregulators may need to be redesigned to accomodate the different RBS's.

==References==

Desai SK and Gallivan JP. Genetic Screens and Selections for Small Molecules Based on a Synthetic Riboswitch That Activates Protein Translation. J. Am. Chem. Soc.(2004) 126:13247-54.

[[Post-transcriptional Regulation Technologies - Erin Zwack|Return to Post-transcriptional Regulation Technologies]]

Riboswitches

2007-12-11T15:42:19Z

WikiSysop: /* From Concept to Wet Lab */

All information on Riboswitches came from Desai and Gallivan (2004).

Riboswitches are small sequences in mRNA that bind small molecules to regulate translation and occasionally transcription. Riboswitches occur naturally in both eukaryotes and prokaryotes. Desai and Gallivan hoped to find new synthetic riboswitches (riboswitches with new ligand specificities) by creating libraries of mutant riboswitches and using genetic selection to pick the functional ones of interest. Desai and Gallivan also employed riboswitches to screen for the presence of specific small molecules. In theory, riboswitches are perfect because the number of aptamers already in existence and our capbaility to engineer new aptamers through rational design provide great versatility in shoosing stimuli and conditions.

==Design==

Reviews of previous research showed that the theophylline aptamer worked in riboswitches in wheat germ, a eukaryote, and ''Bacillus subtilis'', a Gram positive bacterium. Desai and Gallivan decided to translate the technology to Gram negative bacteria. To do this they cloned the theophylline aptamer five base pairs upstream of the RBS for ''lacZ'', a gene that produces the enzyme beta-galactosidase. The gene is controlled by a weak promoter and a weak RBS allowing for sensitivity to changes in translation because of the presence of theophylline. The construct was then transformed into ''E. coli''. When theophylline is added, translation should be turned on again.

==From Concept to Wet Lab==

The aptamer for theophylline was inserted in front of the gene for beta-galactosidase gene to create an mRNA with a riboswitch. Beta-galactosidase activity is dependent on the amount of the enzyme beta-galactosidase; therefore, measurements of beta-galactosidase activity indicate whether translation of the mRNA is taking place. Testing and comparing beta-galactosidase activity for cells with the riboswitch in the presence of theophylline, caffeine, and no ligand as well as testing and comparing beta-galactosidase activity for cells without the riboswitch under the same conditions demonstrated that the riboswitch does control gene expression in response to theophylline (figure 14). Significant increases in beta-galactosidase activity in cells with the riboswitch were only found when theophylline was added. Because beta-galactosidase activity in the presence of theophylline, caffeine, or no ligand for cells without a riboswitch did not significantly vary, the increase in beta-galactosidase activity in the presence of theophylline for cells with a riboswitch most likely resulted from the theophylline binding to the riboswitch and allowing translation.

[[Image:Plate.JPG]][[Image:Beta.JPG]][[Image:Beta_bar.JPG]]

(Desai and Gallivan 2004)

'''Figure 14:''' '''a)''' On the plate are cells with the riboswitch that were grown in three different conditions: theophylline present, caffeine present, and no ligand present. Cells that were grown in the presence of theophylline have a distinct blue/green color, which indicates beta-galactosidase activity, compared to the cells grown with caffeine or without ligand. The lack of color in the cells grown with caffeine or without ligand indicates that the riboswitch most likely prevents translation of the mRNA encoding beta-galactosidase. The blue color of the cells grown with theophylline indicates that theophylline probably is binding to the riboswitch and allowing for translation.''' b)''' The green line represents cells that have had theophylline added to them while the red line represents cells that had caffeine added. Beta-galactosidase activity's increasing, as measured by Miller Units, significantly only in the presence of high enough concentrations of theophylline but not caffeine suggests that the riboswitch is highly specific for its ligand. '''c)''' When no riboswitch existed in the beta-galactosidase transcript, beta-galactosidase activity was not significantly different for cells grown in the presence of theophylline, caffeine, or no ligand. When a riboswitch existed in the beta-galactosidase transcript, cells grown in the presence of theophylline exhibited significantly greater beta-galactosidase activity than cells grown in the presence of caffeine or no ligand. Beta-galactosidase activity for cells grown in the presence of caffeine did not vary significantly from activity for cells grown without any ligand present.

To further support that the theophylline was interacting with the aptamer and not just increasing protein translation through another route, a single point mutation that ''in vitro'' decreases affinity for theophylline and increases affinity for 3-methylxanthine was introduced to the aptamer. When using this mutant riboswitch, the beta-galactosidase activity, in the presence of theophylline was almost the same as beta-galactosidase activity without any small molecule while beta-galactosidase in the presence of 3-methylxanthine was significantly higher than base-line. These results suggest that the change in translation as indicated by the increase of beta-galactosidase is controlled by the riboswitch and not some other mechanism (figure 15).

[[Image:Mutation.JPG]]

(Desai and Gallivan 2004)

'''Figure 15:''' Beta-galactosidase activity was low and basically the same for cells with the mutant riboswitch that had no molecule (black), caffeine (red), or theophylline (green) added. Cells with the mutant riboswitch showed significant increase in beta-galactosidase activity when 3-methylxanthine (blue) was added.

In synthetic biology, parts are often tweaked until the efficiency, detectability, and other properties are enhanced to work as a part in a device. Gallivan's lab worked on optimization of the riboswitch by changing the position of the aptamer in relation to the RBS. The riboswitch showed a greater increase in beta-galactosidase activity when the aptamer was 8 base pairs upstream of the RBS than when the aptamer was either 2 or 5 base pairs upstream (figure 16). This result could be because of the surrounding bases being purines in this particular transcript or the actual distance.

[[Image:Optimization.JPG]]

(Desai and Gallivan 2004)

'''Figure 16:''' Cells exposed to theophylline (green) showed greater beta-galactosidase activity than cells exposed to caffeine (red) or cells exposed to nothing (black) no matter the distance of the riboswitch from the RBS, but the greatest increase was experienced by cells with the riboswitch 8 base pairs (the farthest tested distance) from the RBS when exposed to theophylline.

==Modularity==

An advantage of riboswitches is that they are modular. The aptamer that is in front of the RBS does not have the restriction that it only works in front of specific sequences. A riboswitch can technically be integrated into any mRNA without redesigning the whole riboswitch component unlike with antiswitches. Because riboswitches do not have a complement to any specific RBS, they can function in front of RBS's that vary drastically from each other while Isaac's riboregulators may need to be redesigned to accomodate the different RBS's.

==References==

Desai SK and Gallivan JP. Genetic Screens and Selections for Small Molecules Based on a Synthetic Riboswitch That Activates Protein Translation. J. Am. Chem. Soc.(2004) 126:13247-54.

[[Post-transcriptional Regulation Technologies - Erin Zwack|Return to Post-transcriptional Regulation Technologies]]

Riboswitches

2007-12-11T15:41:34Z

WikiSysop: /* Design */

All information on Riboswitches came from Desai and Gallivan (2004).

Riboswitches are small sequences in mRNA that bind small molecules to regulate translation and occasionally transcription. Riboswitches occur naturally in both eukaryotes and prokaryotes. Desai and Gallivan hoped to find new synthetic riboswitches (riboswitches with new ligand specificities) by creating libraries of mutant riboswitches and using genetic selection to pick the functional ones of interest. Desai and Gallivan also employed riboswitches to screen for the presence of specific small molecules. In theory, riboswitches are perfect because the number of aptamers already in existence and our capbaility to engineer new aptamers through rational design provide great versatility in shoosing stimuli and conditions.

==Design==

Reviews of previous research showed that the theophylline aptamer worked in riboswitches in wheat germ, a eukaryote, and ''Bacillus subtilis'', a Gram positive bacterium. Desai and Gallivan decided to translate the technology to Gram negative bacteria. To do this they cloned the theophylline aptamer five base pairs upstream of the RBS for ''lacZ'', a gene that produces the enzyme beta-galactosidase. The gene is controlled by a weak promoter and a weak RBS allowing for sensitivity to changes in translation because of the presence of theophylline. The construct was then transformed into ''E. coli''. When theophylline is added, translation should be turned on again.

==From Concept to Wet Lab==

The aptamer for theophylline was inserted in front of the gene for beta-galactosidase gene to create an mRNA with a riboswitch. Beta-dalactosidase activity is dependent on the amount of the enzyme beta-galactosidase; therefore, measurements of beta-galactosidase activity indicate whether translation of the mRNA is taking place. Testing and comparing beta-galactosidase activity for cells with the riboswitch in the presence of theophylline, caffeine, and no ligand as well as testing and comparing beta-galactosidase activity for cells without the riboswitch under the same conditions demonstrated that the riboswitch does control gene expression in response to theophylline (figure 14). Significant increases in beta-galactosidase activity in cells with the riboswitch were only found when theophylline was added. Because beta-galactosidase activity in the presence of theophylline, caffeine, or no ligand for cells without a riboswitch did not significantly vary, the increase in beta-galactosidase activity in the presence of theophylline for cells with a riboswitch most likely resulted from the theophylline binding to the riboswitch and allowing translation.

[[Image:Plate.JPG]][[Image:Beta.JPG]][[Image:Beta_bar.JPG]]

(Desai and Gallivan 2004)

'''Figure 14:''' '''a)''' On the plate are cells with the riboswitch that were grown in three different conditions: theophylline present, caffeine present, and no ligand present. Cells that were grown in the presence of theophylline have a distinct blue/green color, which indicates beta-galactosidase activity, compared to the cells grown with caffeine or without ligand. The lack of color in the cells grown with caffeine or without ligand indicates that the riboswitch most likely prevents translation of the mRNA encoding beta-galactosidase. The blue color of the cells grown with theophylline indicates that theophylline probably is binding to the riboswitch and allowing for translation.''' b)''' The green line represents cells that have had theophylline added to them while the red line represents cells that had caffeine added. Beta-galactosidase activity's increasing, as measured by Miller Units, significantly only in the presence of high enough concentrations of theophylline but not caffeine suggests that the riboswitch is highly specific for its ligand. '''c)''' When no riboswitch existed in the beta-galactosidase transcript, beta-galactosidase activity was not significantly different for cells grown in the presence of theophylline, caffeine, or no ligand. When a riboswitch existed in the beta-galactosidase transcript, cells grown in the presence of theophylline exhibited significantly greater beta-galactosidase activity than cells grown in the presence of caffeine or no ligand. Beta-galactosidase activity for cells grown in the presence of caffeine did not vary significantly from activity for cells grown without any ligand present.

To further support that the theophylline was interacting with the aptamer and not just increasing protein translation through another route, a single point mutation that ''in vitro'' decreases affinity for theophylline and increases affinity for 3-methylxanthine was introduced to the aptamer. When using this mutant riboswitch, the beta-galactosidase activity, in the presence of theophylline was almost the same as beta-galactosidase activity without any small molecule while beta-galactosidase in the presence of 3-methylxanthine was significantly higher than base-line. These results suggest that the change in translation as indicated by the increase of beta-galactosidase is controlled by the riboswitch and not some other mechanism (figure 15).

[[Image:Mutation.JPG]]

(Desai and Gallivan 2004)

'''Figure 15:''' Beta-galactosidase activity was low and basically the same for cells with the mutant riboswitch that had no molecule (black), caffeine (red), or theophylline (green) added. Cells with the mutant riboswitch showed significant increase in beta-galactosidase activity when 3-methylxanthine (blue) was added.

In synthetic biology, parts are often tweaked until the efficiency, detectability, and other properties are enhanced to work as a part in a device. Gallivan's lab worked on optimization of the riboswitch by changing the position of the aptamer in relation to the RBS. The riboswitch showed a greater increase in beta-galactosidase activity when the aptamer was 8 base pairs upstream of the RBS than when the aptamer was either 2 or 5 base pairs upstream (figure 16). This result could be because of the surrounding bases being purines in this particular transcript or the actual distance.

[[Image:Optimization.JPG]]

(Desai and Gallivan 2004)

'''Figure 16:''' Cells exposed to theophylline (green) showed greater beta-galactosidase activity than cells exposed to caffeine (red) or cells exposed to nothing (black) no matter the distance of the riboswitch from the RBS, but the greatest increase was experienced by cells with the riboswitch 8 base pairs (the farthest tested distance) from the RBS when exposed to theophylline.

==Modularity==

An advantage of riboswitches is that they are modular. The aptamer that is in front of the RBS does not have the restriction that it only works in front of specific sequences. A riboswitch can technically be integrated into any mRNA without redesigning the whole riboswitch component unlike with antiswitches. Because riboswitches do not have a complement to any specific RBS, they can function in front of RBS's that vary drastically from each other while Isaac's riboregulators may need to be redesigned to accomodate the different RBS's.

==References==

Desai SK and Gallivan JP. Genetic Screens and Selections for Small Molecules Based on a Synthetic Riboswitch That Activates Protein Translation. J. Am. Chem. Soc.(2004) 126:13247-54.

[[Post-transcriptional Regulation Technologies - Erin Zwack|Return to Post-transcriptional Regulation Technologies]]

Riboswitches

2007-12-11T15:41:07Z

WikiSysop: /* Design */

All information on Riboswitches came from Desai and Gallivan (2004).

Riboswitches are small sequences in mRNA that bind small molecules to regulate translation and occasionally transcription. Riboswitches occur naturally in both eukaryotes and prokaryotes. Desai and Gallivan hoped to find new synthetic riboswitches (riboswitches with new ligand specificities) by creating libraries of mutant riboswitches and using genetic selection to pick the functional ones of interest. Desai and Gallivan also employed riboswitches to screen for the presence of specific small molecules. In theory, riboswitches are perfect because the number of aptamers already in existence and our capbaility to engineer new aptamers through rational design provide great versatility in shoosing stimuli and conditions.

==Design==

Reviews of previous research showed that the theophylline aptamer worked in riboswitches in wheat germ, a eukaryote, and ''Bacillus subtilis'', a Gram positive bacterium. Desai and Gallivan decided to translate the technology to gram negative bacteria. To do this they cloned the theophylline aptamer five base pairs upstream of the RBS for ''lacZ'', a gene that produces the enzyme beta-galactosidase. The gene is controlled by a weak promoter and a weak RBS allowing for sensitivity to changes in translation because of the presence of theophylline. The construct was then transformed into ''E. coli''. When theophylline is added, translation should be turned on again.

==From Concept to Wet Lab==

The aptamer for theophylline was inserted in front of the gene for beta-galactosidase gene to create an mRNA with a riboswitch. Beta-dalactosidase activity is dependent on the amount of the enzyme beta-galactosidase; therefore, measurements of beta-galactosidase activity indicate whether translation of the mRNA is taking place. Testing and comparing beta-galactosidase activity for cells with the riboswitch in the presence of theophylline, caffeine, and no ligand as well as testing and comparing beta-galactosidase activity for cells without the riboswitch under the same conditions demonstrated that the riboswitch does control gene expression in response to theophylline (figure 14). Significant increases in beta-galactosidase activity in cells with the riboswitch were only found when theophylline was added. Because beta-galactosidase activity in the presence of theophylline, caffeine, or no ligand for cells without a riboswitch did not significantly vary, the increase in beta-galactosidase activity in the presence of theophylline for cells with a riboswitch most likely resulted from the theophylline binding to the riboswitch and allowing translation.

[[Image:Plate.JPG]][[Image:Beta.JPG]][[Image:Beta_bar.JPG]]

(Desai and Gallivan 2004)

'''Figure 14:''' '''a)''' On the plate are cells with the riboswitch that were grown in three different conditions: theophylline present, caffeine present, and no ligand present. Cells that were grown in the presence of theophylline have a distinct blue/green color, which indicates beta-galactosidase activity, compared to the cells grown with caffeine or without ligand. The lack of color in the cells grown with caffeine or without ligand indicates that the riboswitch most likely prevents translation of the mRNA encoding beta-galactosidase. The blue color of the cells grown with theophylline indicates that theophylline probably is binding to the riboswitch and allowing for translation.''' b)''' The green line represents cells that have had theophylline added to them while the red line represents cells that had caffeine added. Beta-galactosidase activity's increasing, as measured by Miller Units, significantly only in the presence of high enough concentrations of theophylline but not caffeine suggests that the riboswitch is highly specific for its ligand. '''c)''' When no riboswitch existed in the beta-galactosidase transcript, beta-galactosidase activity was not significantly different for cells grown in the presence of theophylline, caffeine, or no ligand. When a riboswitch existed in the beta-galactosidase transcript, cells grown in the presence of theophylline exhibited significantly greater beta-galactosidase activity than cells grown in the presence of caffeine or no ligand. Beta-galactosidase activity for cells grown in the presence of caffeine did not vary significantly from activity for cells grown without any ligand present.

To further support that the theophylline was interacting with the aptamer and not just increasing protein translation through another route, a single point mutation that ''in vitro'' decreases affinity for theophylline and increases affinity for 3-methylxanthine was introduced to the aptamer. When using this mutant riboswitch, the beta-galactosidase activity, in the presence of theophylline was almost the same as beta-galactosidase activity without any small molecule while beta-galactosidase in the presence of 3-methylxanthine was significantly higher than base-line. These results suggest that the change in translation as indicated by the increase of beta-galactosidase is controlled by the riboswitch and not some other mechanism (figure 15).

[[Image:Mutation.JPG]]

(Desai and Gallivan 2004)

'''Figure 15:''' Beta-galactosidase activity was low and basically the same for cells with the mutant riboswitch that had no molecule (black), caffeine (red), or theophylline (green) added. Cells with the mutant riboswitch showed significant increase in beta-galactosidase activity when 3-methylxanthine (blue) was added.

In synthetic biology, parts are often tweaked until the efficiency, detectability, and other properties are enhanced to work as a part in a device. Gallivan's lab worked on optimization of the riboswitch by changing the position of the aptamer in relation to the RBS. The riboswitch showed a greater increase in beta-galactosidase activity when the aptamer was 8 base pairs upstream of the RBS than when the aptamer was either 2 or 5 base pairs upstream (figure 16). This result could be because of the surrounding bases being purines in this particular transcript or the actual distance.

[[Image:Optimization.JPG]]

(Desai and Gallivan 2004)

'''Figure 16:''' Cells exposed to theophylline (green) showed greater beta-galactosidase activity than cells exposed to caffeine (red) or cells exposed to nothing (black) no matter the distance of the riboswitch from the RBS, but the greatest increase was experienced by cells with the riboswitch 8 base pairs (the farthest tested distance) from the RBS when exposed to theophylline.

==Modularity==

An advantage of riboswitches is that they are modular. The aptamer that is in front of the RBS does not have the restriction that it only works in front of specific sequences. A riboswitch can technically be integrated into any mRNA without redesigning the whole riboswitch component unlike with antiswitches. Because riboswitches do not have a complement to any specific RBS, they can function in front of RBS's that vary drastically from each other while Isaac's riboregulators may need to be redesigned to accomodate the different RBS's.

==References==

Desai SK and Gallivan JP. Genetic Screens and Selections for Small Molecules Based on a Synthetic Riboswitch That Activates Protein Translation. J. Am. Chem. Soc.(2004) 126:13247-54.

[[Post-transcriptional Regulation Technologies - Erin Zwack|Return to Post-transcriptional Regulation Technologies]]

Riboswitches

2007-12-11T15:40:40Z

WikiSysop:

All information on Riboswitches came from Desai and Gallivan (2004).

Riboswitches are small sequences in mRNA that bind small molecules to regulate translation and occasionally transcription. Riboswitches occur naturally in both eukaryotes and prokaryotes. Desai and Gallivan hoped to find new synthetic riboswitches (riboswitches with new ligand specificities) by creating libraries of mutant riboswitches and using genetic selection to pick the functional ones of interest. Desai and Gallivan also employed riboswitches to screen for the presence of specific small molecules. In theory, riboswitches are perfect because the number of aptamers already in existence and our capbaility to engineer new aptamers through rational design provide great versatility in shoosing stimuli and conditions.

==Design==

Reviews of previous research showed that the theophylline aptamer worked in riboswitches in wheat germ, a eukaryote, and ''Bacillus subtilis'', a gram positive bacterium. Desai and Gallivan decided to translate the technology to gram negative bacteria. To do this they cloned the theophylline aptamer five base pairs upstream of the RBS for ''lacZ'', a gene that produces the enzyme beta-galactosidase. The gene is controlled by a weak promoter and a weak RBS allowing for sensitivity to changes in translation because of the presence of theophylline. The construct was then transformed into ''E. coli''. When theophylline is added, translation should be turned on again.

==From Concept to Wet Lab==

The aptamer for theophylline was inserted in front of the gene for beta-galactosidase gene to create an mRNA with a riboswitch. Beta-dalactosidase activity is dependent on the amount of the enzyme beta-galactosidase; therefore, measurements of beta-galactosidase activity indicate whether translation of the mRNA is taking place. Testing and comparing beta-galactosidase activity for cells with the riboswitch in the presence of theophylline, caffeine, and no ligand as well as testing and comparing beta-galactosidase activity for cells without the riboswitch under the same conditions demonstrated that the riboswitch does control gene expression in response to theophylline (figure 14). Significant increases in beta-galactosidase activity in cells with the riboswitch were only found when theophylline was added. Because beta-galactosidase activity in the presence of theophylline, caffeine, or no ligand for cells without a riboswitch did not significantly vary, the increase in beta-galactosidase activity in the presence of theophylline for cells with a riboswitch most likely resulted from the theophylline binding to the riboswitch and allowing translation.

[[Image:Plate.JPG]][[Image:Beta.JPG]][[Image:Beta_bar.JPG]]

(Desai and Gallivan 2004)

'''Figure 14:''' '''a)''' On the plate are cells with the riboswitch that were grown in three different conditions: theophylline present, caffeine present, and no ligand present. Cells that were grown in the presence of theophylline have a distinct blue/green color, which indicates beta-galactosidase activity, compared to the cells grown with caffeine or without ligand. The lack of color in the cells grown with caffeine or without ligand indicates that the riboswitch most likely prevents translation of the mRNA encoding beta-galactosidase. The blue color of the cells grown with theophylline indicates that theophylline probably is binding to the riboswitch and allowing for translation.''' b)''' The green line represents cells that have had theophylline added to them while the red line represents cells that had caffeine added. Beta-galactosidase activity's increasing, as measured by Miller Units, significantly only in the presence of high enough concentrations of theophylline but not caffeine suggests that the riboswitch is highly specific for its ligand. '''c)''' When no riboswitch existed in the beta-galactosidase transcript, beta-galactosidase activity was not significantly different for cells grown in the presence of theophylline, caffeine, or no ligand. When a riboswitch existed in the beta-galactosidase transcript, cells grown in the presence of theophylline exhibited significantly greater beta-galactosidase activity than cells grown in the presence of caffeine or no ligand. Beta-galactosidase activity for cells grown in the presence of caffeine did not vary significantly from activity for cells grown without any ligand present.

To further support that the theophylline was interacting with the aptamer and not just increasing protein translation through another route, a single point mutation that ''in vitro'' decreases affinity for theophylline and increases affinity for 3-methylxanthine was introduced to the aptamer. When using this mutant riboswitch, the beta-galactosidase activity, in the presence of theophylline was almost the same as beta-galactosidase activity without any small molecule while beta-galactosidase in the presence of 3-methylxanthine was significantly higher than base-line. These results suggest that the change in translation as indicated by the increase of beta-galactosidase is controlled by the riboswitch and not some other mechanism (figure 15).

[[Image:Mutation.JPG]]

(Desai and Gallivan 2004)

'''Figure 15:''' Beta-galactosidase activity was low and basically the same for cells with the mutant riboswitch that had no molecule (black), caffeine (red), or theophylline (green) added. Cells with the mutant riboswitch showed significant increase in beta-galactosidase activity when 3-methylxanthine (blue) was added.

In synthetic biology, parts are often tweaked until the efficiency, detectability, and other properties are enhanced to work as a part in a device. Gallivan's lab worked on optimization of the riboswitch by changing the position of the aptamer in relation to the RBS. The riboswitch showed a greater increase in beta-galactosidase activity when the aptamer was 8 base pairs upstream of the RBS than when the aptamer was either 2 or 5 base pairs upstream (figure 16). This result could be because of the surrounding bases being purines in this particular transcript or the actual distance.

[[Image:Optimization.JPG]]

(Desai and Gallivan 2004)

'''Figure 16:''' Cells exposed to theophylline (green) showed greater beta-galactosidase activity than cells exposed to caffeine (red) or cells exposed to nothing (black) no matter the distance of the riboswitch from the RBS, but the greatest increase was experienced by cells with the riboswitch 8 base pairs (the farthest tested distance) from the RBS when exposed to theophylline.

==Modularity==

An advantage of riboswitches is that they are modular. The aptamer that is in front of the RBS does not have the restriction that it only works in front of specific sequences. A riboswitch can technically be integrated into any mRNA without redesigning the whole riboswitch component unlike with antiswitches. Because riboswitches do not have a complement to any specific RBS, they can function in front of RBS's that vary drastically from each other while Isaac's riboregulators may need to be redesigned to accomodate the different RBS's.

==References==

Desai SK and Gallivan JP. Genetic Screens and Selections for Small Molecules Based on a Synthetic Riboswitch That Activates Protein Translation. J. Am. Chem. Soc.(2004) 126:13247-54.

[[Post-transcriptional Regulation Technologies - Erin Zwack|Return to Post-transcriptional Regulation Technologies]]

Riboregulators

2007-12-11T15:30:33Z

WikiSysop: /* From Concept to Wet Lab */

All information on riboregulators came from Isaacs et al. 2004.

After observing the many and varied naturally occuring post-transcriptional, regulatory RNA systems, Isaacs et al. designed and engineered a modular synthetic system where RNA turns on and off gene expression by controlling translation. The modularity of their system allows any gene to be regulated instead of only a specific gene to which the riboregulator is targeted.

==Design==

The design itself has two components: a short cis-repressed RNA sequence (crRNA) that is inserted upstream of the gene and a transactivating RNA sequence (taRNA) that targets the crRNA and is free floating. The crRNA sequence contains two fundamental components: the complement of the ribosomal binding site (RBS) and a pyrimidine-uracil-nucleotide-purine (YUNR) sequence (figure 9). When not interacting with the taRNA, the complement of the RBS binds to the RBS, causing the crRNA to loop and block the ribosome's access to the RBS. When the RBS is blocked, translation does not occur; the gene expression is off. The YUNR sequence has a complement on the taRNA. When the taRNA finds a crRNA, the interaction with the YUNR sequence begins pulling the crRNA off the RBS (figure 11).

[[Image:CrRNA.JPG ]]

(Isaacs et al. 2004)

'''Figure 9:''' Cis repressing RNA, crRNA, sequence is inserted upstream of the RBS. Part of the crRNA (red) complements the RBS and a few bases on either side. This section of the crRNA is not an exact complement; thus, the crRNA can be peeled off the RBS by the trans activating RNA, taRNA. The complementary sequence to YUNR is found in the ta-RNA and begins peeling off the crRNA by binding to it (figure 11).

The taRNA is free floating and regulated by an inducible promoter. The inducible promoter allows the researcher to determine under what conditions translation will be allowed. Besides the complement to the YUNR sequence, the taRNA contains a section of high complementarity to the section of the crRNA that folds over to cover the RBS. This complementary sequence allows the taRNA to keep the crRNA sequestered from the RBS (figure 10).

[[Image:TaRNA.JPG]]

(Isaacs et al. 2004)

'''Figure 10:''' taRNA when not in contact with crRNA sequesters the complement to the crRNA as it is extremely close to the RBS sequence. If the complement to the crRNA was not sequestered until the taRNA comes in contact with the YUNR sequence, ribosomes could potentially bind to the taRNA and decrease the efficiency of translation in the cell. When the YUNR complement binds to the YUNR sequence in the cr-RNA, the binding of the crRNA to the rest of its complement in the taRNA begins, and the crRNA is pulled off the RBS (figure 11).

To recap, gene expression is off when there is crRNA upstream of the gene but no taRNA is in the system. In the presence of taRNA, gene expression is turned back on.

[[Image:Riboregulator system.JPG ]]

(Isaacs et al. 2004)

'''Figure 11:''' a. the crRNA is bound to the RBS and blocks ribosomes from translating the mRNA into protein. b. the taRNA comes in contact with the YUNR sequence on the crRNA and begins to bind to the rest of the crRNA. c. the taRNA fully bound to the crRNA and peeled the crRNA off the RBS. The RBS is now free for the ribosome to bind to and begin translation.

==From Concept to Wet Lab==

In wet lab, the crRNA sequence was placed in front of the GFP gene and introduced to cells. Using flow cytometry, Isaacs ''et al.'' measured fluorescence of cells that had just the GFP gene, the GFP gene with crRNA upstream, the GFP gene with crRNA upstream and taRNA, and no GFP at all. The crRNA decreased fluorescence to near basal levels. When taRNA was present in the system, fluorescence increased by approximately a power of ten. While the fluorescence did not equal fluorescence of cells with just GFP, the repression of gene expression with crRNA and return of expression with taRNA are strong enough to suggest the riboregulator system works (figure 12).

[[Image:experimental_taRNA.JPG]]

(Isaacs et al. 2004)

'''Figure 12:''' The black curve represents fluorescence in cells that do not have a GFP gene (ie. the cells natural autofluorescence). The red curve represents fluorescence in cells that have GFP with crRNA upstream. The green curve represents fluorescence in cells that have GFP with crRNA upstream and produce the taRNA. The blue curve represents the fluorescence of cells that have normal GFP minus the cells' autofluorescence.

Finally, riboregulators can control different genes in response to different stimuli by using different crRNA/taRNA pairs as the pairs are specific to each other (figure 13).

[[Image: Specific.JPG]]

(Isaacs et al. 2004)

'''Figure 13:''' The graph shows both GFP fluorescence (black and white bars) when the taRNA promoter, pBad, is off (- arabinose) and on (+ arabinose). All data is normalized to + arabinose GFP and RNA levels. The low level GFP fluorescence when no arabinose is present shows that the efficiency of crRNA is not 100% but is still high. As high GFP fluorescence is seen only when high amounts of the matching taRNA is present and not simply when any taRNA variant is present, this data shows that riboregulator pairs are specific and multiple pairs can be used to regulate multiple genes without fear that any taRNA produced would activate all the RNAs and not just its target RNA.

==Modularity==

While all of these experiments were done with the GFP gene, Isaacs et al. designed their riboregulator to be modular, capable of being used with any gene (Isaacs et al. 2004). As the taRNA simply targets the crRNA and the crRNA can be placed in front of any gene, it can be considered modular. The above experiments were done with pBad and pLac controlling the production of taRNA. As the system worked when the taRNA was under control of either promoter and the crRNA can be inserted upstream of any gene, this system is basically considered modular.

The only caveat is that the crRNA construct added to the gene will need to contain the RBS unless the gene's RBS is close enough to the complement to bind to it. Even small changes to a ribosomal binding site can can change the transcription rate of genes (Gardner et al. 2000). If the original RBS is not close enough to the complement in the crRNA and you desire to keep the original transcriptional rate and level, you would have to redesign the crRNA and the taRNA as the complement to the RBS in the crRNA and the complement to the crRNA in the taRNA would need to be different.

==Further Work==

As mentioned in the overview, using regulatory proteins or inducible promoters limits the number of stimuli (molecules) that can be used to determine when expression should occur. Generating taRNA that is ligand controlled like an antiswitch or riboswitch would provide a greater versatility for riboregulator use. Ligand controlled riboregulators may also be more effective as control by a ligand may decrease leaky activity, which occurs with promoters as they always allow for some basal level of transcription. Isaacs has continued working to engineer a ligand controlled version of the riboregulator.

==References==

Gardner, T.S., Cantor, C.R., and Collins, J.J. Construction of a genetic toggle switch in Eschreichia coli. Nature (2000) 403: 339-342.

Isaacs FJ, et al. Engineered riboregulators enable post-transcriptional control of gene expression. Nat Biotechnol. (2004) 22:841-47.

[[Post-transcriptional Regulation Technologies - Erin Zwack|Return to Post-transcriptional Regulation Technologies]]

Antiswitches

2007-12-11T14:51:37Z

WikiSysop: /* From Concept to Wet Lab */

All information about antiswitches came from Bayer and Smolke (2005).

Antiswitches, trans-RNA molecules that regulate translation of mRNA based on the presence or absence of specific ligands, were first developed by Smolke and Bayer (2005) in ''Saccharomyces cerevisiae''. Two types of antiswitches were engineered: on-switches and off-switches. On-switches turn on protein expression in the presence of the ligand while off-switches turn off protein expression in the presence of the ligand. Control by the ligand allows researchers to regulate pathways’ protein production with less leakiness, low level transcription even when “off”, than using a specific promoter.

==Design==

Antiswitches are made of an aptamer and two stems: the aptamer stem and the antisense stem (figure 3).

[[Image:Switch.JPG]]

(Bayer and Smolke 2005 Permission Pending)

'''Figure 3:''' The antisense stem can bind to itself (duplex). The antisense stem also contains the complement to the RNA that a researcher desires to regulate. The aptamer stem swings either toward the antisense stem and disrupts the duplex or swings away from the antisense stem and allows the antisense stem to duplex with itself depending on whether the aptamer is bound to its ligand.

The aptamer is the sequence that binds the ligand and causes a conformational (shape) change of the antiswitch molecule. Aptamers can be highly specific for their particular molecules. The theophylline aptamer used by Smolke and Bayer can distinguish between caffeine and theophylline, which differ by a single methyl (figure 4).

[[Image:Caffeine.JPG]]

'''Figure 4.''' Caffeine (a) and Theophylline (b) differ by one methyl group (circled in red).

While Smolke and Bayer mainly used the aptamer for the ligand theophylline, aptamers for many other molecules are now being generated using rational design (Win and Smolke 2007). The large number of possible aptamers provides versatility in what will control gene expression.

The antisense stem contains a sequence that complements a targeted RNA transcript and a second sequence that sequesters this complementary sequence to keep it from binding the transcript. When the antisense stem is not duplexed with itself, it prevents translation by binding to the complementary mRNA (figure 5).

The aptamer stem is a short sequence that complements a portion of the sequestering sequence of the antisense stem. In an off-switch, the aptamer stem swings towards the antisense stem and displaces the portion that complements the targeted transcript when the liand binds to the aptamer (figure 5). In an on-switch, the aptamer swings away from the antisense stem when the ligand binds to the aptamer; thus, the antisense stem can duplex and is no longer free to bind to the transcript.

[[Image:ANTI.jpg]]

(Bayer and Smolke 2005 Permission Pending)

'''Figure 5:''' a) An inactive off-switch. The antisense stem is duplexed with itself. b) The same switch after being activated by theophylline (blue ellipse). The antisense stem is duplexed with the mRNA; thus, translation is stopped.

==From Concept to Wet Lab==

Smolke and Bayer synthesized genes for antiswitches with antisense stems that contained complements to either GFP or YFP transcripts and the aptamer to either theophylline or tetracycline. These genes were then transformed into ''Saccharomyces cerevisiae''. Several experiments, including dose-response curves to the appropriate ligand and fluorescence measurements of cells when exposed to both the appropriate ligand and wrong ligand, demonstrated that these antiswitches effectively regulate translation of their specific target in response to only the correct ligand.

In the dose-response experiments for off-switches when enough ligand (~ 1 mM theophylline or tetracycline depending on the switch) was present, the inactive off-switches became active and relative GFP expression dropped to almost zero. These experimental results support the antiswitch technology being functional. As off-switches with either theophylline or tetracycline aptamers worked, the versatility of antiswitches is supported (figure 6).

[[Image:Offswitch_experiment.JPG]]

(Bayer and Smolke 2005 Permission Pending)

'''Figure 6:''' The red line represents the tetracycline controlled off-switch while blue line represents the theophylline controlled off-switch. When the concentration of ligand is high enough, enough off-switches are active and binding to mRNA to stop translation of the gene.

In the dose-response experiments for the on-switches when ~ 1 mM of theophylline was added to cells containing the inactive on-switch, the cells relative expression of GFP jumped from near zero to approximately 0.9. This data show that the on-switch is also functional (figure 7).

[[Image:Onswitch_experiment.JPG]]

(Bayer and Smolke 2005 Permission Pending)

'''Figure 7:''' The red line represents the on-switch while the blue Line represents the off-switch. When a high enough concentration of ligand is present, enough on-switches are activated and have thus released their target mRNA for detectable near normal translation to occur as measured by fluorescence. The off-switch follows the opposite path as shown in figure 6.

Finally, antiswitches in eukaryotes can be combined to regulate multiple genes for different conditions like the [[Riboregulators|taRNA/crRNA system in prokaryotes]]. By simply switching the aptamer and antisense stem targeting sequence, the switch now controls a different gene by a different stimulus. Off-switch experiments using a theophylline aptamer with a GFP targeting antisense stem and a tetracycline aptamer with a YFP targeting antisense stem showed that the switches regulate the genes independently and can be used in combination to create complex regulatory mechanisms (figure 8).

[[Image:Combinatorial.JPG]]

(Bayer and Smolke 2005 Permission Pending)

'''Figure 8:''' a) On the left, theophylline binds to the antiswitch's aptamer and activates the off-switch, which then binds to the GFP transcript and prevents translation. On the right, tetracycline binds to the antiswitch's aptamer and activates the off-switch, which then binds to the YFP transcript and prevents translation. If only one ligand is present (either theophylline or tetracycline), only one gene's transcripts (either GFP or YFP) are not translated. b) When no theophylline or tetracycline is present, both GFP and YFP are expressed at near normal levels. When only tetracycline is present, GFP is expressed at near normal levels while YFP expression is close to zero. When only theophylline is present, YFP is expressed at near normal levels while GFP expression is close to zero. When both tetracycline and theophylline are present, GFP and YFP expression are both close to zero.

==Modularity==

While antiswitches have many advantages, they do lack modularity, the ability to be integrated with any gene without redesigning a new sequence specific part (Isaacs et al. 2004). If you want to regulate a new gene, you have to synthesize a whole new antiswitch because the antisense stem is specific to a particular gene and the aptamer stem is specific to a particular antisense stem.

==References==

Bayer TS and Smolke CD. Programmable ligand-controlled riboregulators of eukaryotic gene expression. Nat Biotechnol. (2005) 3:337-43.

Isaacs FJ, et al. Engineered riboregulators enable post-transcriptional control of gene expression. Nat Biotechnol. (2004) 22:841-47.

Win MN, Smolke CD (2007). A modular and extensible RNA-based gene-regulatory platform for engineering cellular function. PNAS 104(36):14283-8. Epub 2007 Aug 20. [http://www.pnas.org/cgi/content/abstract/104/36/14283 Abstract].

[[Post-transcriptional Regulation Technologies - Erin Zwack|Return to Post-transcriptional Regulation Technologies]]

Antiswitches

2007-12-11T14:41:47Z

WikiSysop:

All information about antiswitches came from Bayer and Smolke (2005).

Antiswitches, trans-RNA molecules that regulate translation of mRNA based on the presence or absence of specific ligands, were first developed by Smolke and Bayer (2005) in ''Saccharomyces cerevisiae''. Two types of antiswitches were engineered: on-switches and off-switches. On-switches turn on protein expression in the presence of the ligand while off-switches turn off protein expression in the presence of the ligand. Control by the ligand allows researchers to regulate pathways’ protein production with less leakiness, low level transcription even when “off”, than using a specific promoter.

==Design==

Antiswitches are made of an aptamer and two stems: the aptamer stem and the antisense stem (figure 3).

[[Image:Switch.JPG]]

(Bayer and Smolke 2005 Permission Pending)

'''Figure 3:''' The antisense stem can bind to itself (duplex). The antisense stem also contains the complement to the RNA that a researcher desires to regulate. The aptamer stem swings either toward the antisense stem and disrupts the duplex or swings away from the antisense stem and allows the antisense stem to duplex with itself depending on whether the aptamer is bound to its ligand.

The aptamer is the sequence that binds the ligand and causes a conformational (shape) change of the antiswitch molecule. Aptamers can be highly specific for their particular molecules. The theophylline aptamer used by Smolke and Bayer can distinguish between caffeine and theophylline, which differ by a single methyl (figure 4).

[[Image:Caffeine.JPG]]

'''Figure 4.''' Caffeine (a) and Theophylline (b) differ by one methyl group (circled in red).

While Smolke and Bayer mainly used the aptamer for the ligand theophylline, aptamers for many other molecules are now being generated using rational design (Win and Smolke 2007). The large number of possible aptamers provides versatility in what will control gene expression.

The antisense stem contains a sequence that complements a targeted RNA transcript and a second sequence that sequesters this complementary sequence to keep it from binding the transcript. When the antisense stem is not duplexed with itself, it prevents translation by binding to the complementary mRNA (figure 5).

The aptamer stem is a short sequence that complements a portion of the sequestering sequence of the antisense stem. In an off-switch, the aptamer stem swings towards the antisense stem and displaces the portion that complements the targeted transcript when the liand binds to the aptamer (figure 5). In an on-switch, the aptamer swings away from the antisense stem when the ligand binds to the aptamer; thus, the antisense stem can duplex and is no longer free to bind to the transcript.

[[Image:ANTI.jpg]]

(Bayer and Smolke 2005 Permission Pending)

'''Figure 5:''' a) An inactive off-switch. The antisense stem is duplexed with itself. b) The same switch after being activated by theophylline (blue ellipse). The antisense stem is duplexed with the mRNA; thus, translation is stopped.

==From Concept to Wet Lab==

Smolke and Bayer synthesized genes for antiswitches with antisense stems that contained complements to either GFP or YFP transcripts and the aptamer to either theophylline or tetracycline. These genes were then transformed into ''Saccharomyces cerevisiae''. Several experiments, including dose-response curves to the appropriate ligand and fluorescence measurements of cells when exposed to both the appropriate ligand and wrong ligand, demonstrated that these antiswitches effectively regulate translation of their specific target in response to only the correct ligand.

In the dose-response experiments for off-switches when enough ligand (~ 1 mM theophylline or tetracycline depending on the switch) was present, the inactive off-switches became active and relative GFP expression dropped to almost zero. These experimental results support the antiswitch technology being functional. As off-switches with either theophylline or tetracycline aptamers worked, the versatility of antiswitches is supported (figure 6).

[[Image:Offswitch_experiment.JPG]]

(Bayer and Smolke 2005 Permission Pending)

'''Figure 6:''' The red line represents the tetracycline controlled off-switch while blue line represents the theophylline controlled off-switch. When the concentration of ligand is high enough, enough off-switches are active and binding to mRNA to stop translation of the gene.

In the dose-response experiments for the on-switches when ~ 1 mM of theophylline was added to cells containing the inactive on-switch, the cells relative expression of GFP jumped from near zero to approximately 0.9. This data show that the on-switch is also functional (figure 7).

[[Image:Onswitch_experiment.JPG]]

(Bayer and Smolke 2005 Permission Pending)

'''Figure 7:''' The red line represents the on-switch while the blue Line represents the off-switch. When a high enough concentration of ligand is present, enough on-switches are activated and have thus released their target mRNA for detectable near normal translation to occur as measured by fluorescence. The off-switch follows the opposite path as shown in figure 6.

Finally, antiswitches in eukaryotes can be combined to regulate multiple genes for different conditions like the taRNA/crRNA system in prokaryotes. By simply switching the aptamer and antisense stem targeting sequence, the switch now controls a different gene by a different stimulus. Off-switch experiments using a theophylline aptamer with a GFP targeting antisense stem and a tetracycline aptamer with a YFP targeting antisense stem showed that the switches regulate the genes independently and can be used in combination to create complex regulatory mechanisms (figure 8).

[[Image:Combinatorial.JPG]]

(Bayer and Smolke 2005 Permission Pending)

'''Figure 8:''' a) On the left, theophylline binds to the antiswitch's aptamer and activates the off-switch, which then binds to the GFP transcript and prevents translation. On the right, tetracycline binds to the antiswitch's aptamer and activates the off-switch, which then binds to the YFP transcript and prevents translation. If only one ligand is present (either theophylline or tetracycline), only one gene's transcripts (either GFP or YFP) are not translated. b) When no theophylline or tetracycline is present, both GFP and YFP are expressed at near normal levels. When only tetracycline is present, GFP is expressed at near normal levels while YFP expression is close to zero. When only theophylline is present, YFP is expressed at near normal levels while GFP expression is close to zero. When both tetracycline and theophylline are present, GFP and YFP expression are both close to zero.

==Modularity==

While antiswitches have many advantages, they do lack modularity, the ability to be integrated with any gene without redesigning a new sequence specific part (Isaacs et al. 2004). If you want to regulate a new gene, you have to synthesize a whole new antiswitch because the antisense stem is specific to a particular gene and the aptamer stem is specific to a particular antisense stem.

==References==

Bayer TS and Smolke CD. Programmable ligand-controlled riboregulators of eukaryotic gene expression. Nat Biotechnol. (2005) 3:337-43.

Isaacs FJ, et al. Engineered riboregulators enable post-transcriptional control of gene expression. Nat Biotechnol. (2004) 22:841-47.

Win MN, Smolke CD (2007). A modular and extensible RNA-based gene-regulatory platform for engineering cellular function. PNAS 104(36):14283-8. Epub 2007 Aug 20. [http://www.pnas.org/cgi/content/abstract/104/36/14283 Abstract].

[[Post-transcriptional Regulation Technologies - Erin Zwack|Return to Post-transcriptional Regulation Technologies]]

Post-transcriptional Regulation Technologies - Erin Zwack

2007-12-11T14:36:09Z

WikiSysop: /* Overview: */

==Post-transcriptional Regulation Technologies==

==Overview:==

Regulation of translation provides an excellent tool for research on metabolic and other pathways in organisms, and for the production of different sensors by controlling the translation of specific genes depending on cellular conditions. Further development of these types of technologies could provide a “knock-down” equivalent to RNAi, which exist in some eukaryotes. A gene of interest could be expressed normally at all times when the regulator is not active; thus, no ill effects will result before a particular pathway activates and produces a specific ligand if the gene has another purpose as well. Other synthetic biologists could use these technologies to engineer fast-responding, RNA-based biological sensors for environmental chemicals, or novel pathways.

Using RNA regulatory molecules instead of regulatory proteins to control gene expression provides several benefits to synthetic biologists. Regulatory proteins bind to specific sites such as sites on the promoter or sites upstream of the promoter called operators ([http://en.wikipedia.org/wiki/Gene_regulation#Regulatory_protein wikipedia]). Control by these proteins can rely heavily on [[CellularMemory:Mathematical Models#Cooperativity_and_Bistability| cooperativity]] (Gardner et al. 2000), that is multiple proteins binding to one site, in order to see an effect. Regulatory RNA molecules on the other hand need a one to one ratio of regulatory molecule to target. As long as the molecule is expressed in an equal or greater amount than the target, the regulatory RNAs will normally be able to bind to their targets and control transcription.

While synthetic biologists could use the regulatory proteins and their binding sites that are found in nature, rational design of more new regulatory proteins is difficult. With the oligo and gene sythesis technology in existence today, RNA can be engineered that complements and thus targets any other RNA sequence. Regulatory proteins are also controlled mainly by promoter when determining whether they are active or not. As only so many inducible promoters exist, there is a small number of stimuli that can be used to determine under what conditions the gene under a regulatory protein's control will be expressed or repressed. An aptamer, RNA sequence that binds to a small molecule such as theophylline (Bayer and Smolke 2005), can be used not only to regulate gene expression but can also regulate under what conditions an RNA regulatory molecule is active. New aptamers are easily developed through rational design, and the number in existence is continually increasing and providing new molecules that can act as ligands.

Finally, regulatory proteins stop gene expression before transcription. When the stimulus changes and the gene is expressed (either because a regulatory protein has now bound to or has released its site), the time it takes for the phenotype to be expressed is longer because both transcription and translation must occur instead of just translation. With RNA, the gene expression is halted after transcription. Once the stimulus is removed, the RNA already produced by the gene simply needs to be translated.

==Development of Systems==

In most cases, post-transcriptional regulatory mechanisms that were developed and worked in eukaryotes cannot be directly transferred to prokaryotes. Modifications are necessary because eukaryotic and prokaryotic transcription and translation do not follow the exact same path. In eukaryotes, mRNA must have introns spliced out before translation begins; thus, any mechanism that regulates translation has time to bind or manipulate the mRNA (figure 1). In prokaryotes, translation begins as soon as the ribosomal binding site (RBS) is transcribed and accessible to a ribosome (figure 2).

[[Image:Eukaryotic.jpg]]

'''Figure 1:''' Inside of the nucleus of the eukaryote, the gene is transcribed into pre-mRNA, which contain both introns (orange) and exons (red). The pre-mRNA is then modified so that the introns are spliced out and the exons are put together. Finally the mRNA are translated by the ribosome in the cytoplasm.

[[Image:Prokaryote.jpg]]

'''Figure 2:''' In a prokaryotic cell the DNA is transcribed by RNA polymerase. As soon as the polymerase transcribes the ribosomal binding site, a ribosome binds and begins translating the sequence into protein. There is no modification step between transcription and translation.

==Eukaryotes:==

[[Antiswitches]]

==Prokaryotes:==

[[Riboregulators]]

[[Riboswitches]]

==Use of Post-transciptional Regulatory Technologies==

Regulation of translation provides an excellent tool for research on metabolic and other pathways in organisms, and for the production of different sensors by controlling the translation of specific genes depending on cellular conditions. Researchers can turn-off translation of certain genes in response to different pathways being activated, such as metabolic pathways. If a researcher wanted to know if a particular gene were necessary to proper function of a pathway, the aptamer of the antiswitch or riboswitch could be designed to have a molecule produced upstream in the pathway be its ligand. The gene would be expressed normally at all times when the pathway is not active; thus, no ill effects will result before the pathway activates if the gene has another purpose as well. This would provide a “knock-down” equivalent to RNAi, which exists in some eukaryotes.

Synthetic biologists could engineer fast-responding, RNA-based biological sensors for environmental chemicals. The sensors could be used to detect harmful chemical like arsenic or chemical indicative of explosives by using post-transcriptional regulatory technologies that would activate or be produced in the presence of the chemical and turn-on a reporter gene like GFP. The technology could also be used to engineer novel pathways that only activate when the environmental conditions are favorable. By regulating when a newly engineered pathway is on, biologists could possibly achieve optimal efficiency in generating a desired product by not taxing the cells when they do not have enough resources to thrive and produce a product that is not natural to the organism.

==The Labs==

[http://www.che.caltech.edu/groups/cds/index.htm Smolke]

[http://www.bu.edu/abl/ Collins]

[http://www.bu.edu/abl/files/naturebiotech_isaacs.pdf Dr. Isaacs's Review of RNA Synthetic Biology]

[http://gallivan1.chem.emory.edu/Gallivan%20Lab/Home.html Gallivan]

==References==

[http://www.ncbi.nlm.nih.gov/sites/entrez?cmd=Retrieve&db=pubmed&dopt=AbstractPlus&list_uids=15723047 Bayer TS and Smolke CD. Programmable ligand-controlled riboregulators of eukaryotic gene expression. ''Nat Biotechnol''. (2005) 3:337-43.]

[http://pubs.acs.org/cgi-bin/article.cgi/jacsat/2004/126/i41/pdf/ja048634j.pdf Desai SK and Gallivan JP. Genetic Screens and Selections for Small Molecules Based on a Synthetic Riboswitch That Activates Protein Translation. ''J. Am. Chem. Soc.''(2004) 126:13247-54.]

[http://www.bio.davidson.edu/courses/synthetic/papers/Collins_ToggleSwitch.pdf Gardner, T.S., Cantor, C.R., and Collins, J.J. Construction of a genetic toggle switch in Eschreichia coli. ''Nature'' (2000) 403: 339-342.]

Isaacs FJ, et al. Engineered riboregulators enable post-transcriptional control of gene expression. ''Nat Biotechnol.'' (2004) 22:841-47.[http://www.bio.davidson.edu/Courses/Synthetic/papers/RNA_Regulation.pdf]

Regulatory Proteins. Wikipedia. December 2007. http://en.wikipedia.org/wiki/Gene_regulation#Regulatory_protein

[[A Review of Synthetic Biology| Return to Main Page]]

Random Gene Knockout to Maximize Product Yield

2007-12-10T17:11:58Z

WikiSysop: /* Computer Modeling to Increase Product Yield */

__NOTOC__

==The Experiment==

===The Problem===

Researchers Hal Alper, Kohei Miyaoku and Greg Stephanopoulos have recently demonstrated the effectiveness of using directed evolution to optimize a synthetic construct. Previously, the researchers engineered a strain of ''E. coli'' to synthesize lycopene, a carotenoid that naturally occurs in tomatoes. Lycopene is an antioxidant that has recently been incorporated into vitamin tablets, creating a commercial pressure to increase the efficiency and yield of lycopene synthesis.

===Computer Modeling to Increase Product Yield===

To further optimize lycopene production by the ''E. coli'' strain, the authors used computer modling to identify a series of gene knockouts sites that were predicted to increase lycopene production. However, the strains engineered to contain these knockouts were still unable to produced lycopene at the predicted “stoichiometric maximum.” This finding led the researchers to hypothesize that lycopene production was “limited by unknown kinetic or regulatory factors unaccounted for in the stoichiometric models.”

===Directed Evolution: Random Gene Knockout===

The researchers used directed evolution to find other gene knockouts that might affect these unexplored regions of the cell. A library of lycopene-producing E. coli with random gene knockout was achieved by introducing genome-wide integrating transposons to the cells in vivo. This random knockout library was then plated and tested for lycopene production. In this assay, lycopene production was proportional to the red color of bacterial colonies. The three best knockouts selected by this screening were there molecularly characterized. All three knockout sites were differed that had been predicted to be beneficial using their models. Two of these knockouts interrupted genes which had been previously undescribed. Thus, by using a random strategy, they discovered new genes whose expression affected the production of the desired product.

===Computer Modeling Versus Random Gene Knockout===

The team went on to determine the relative effectiveness of the three gene knockouts at increasing lycopene production when compared to the knockouts they had predicting with computer modeling. To answer this question, the researchers created 64 unique strains of the lycopene-producing E. coli representing all possible combinations of the three knockouts selected by directed evolution, the seven model-predicted knockouts, and the two parental strains from which the “evolved” and model-predicted strains were derived. Lycopene production of the 64 strains was measured by extracting the lycopene from the each colony after a defined interval of time and quantifying lycopene level by absorbance spectroscopy at 475. Of the two global maxima of this experiment, one had a knockout selected through directed evolution testing ('''Fig. 1a'''). Furthermore, this particular knockout strain also showed an earlier peak in lycopene production when compared to the completely systematically-predicted knockout strain in batch fed culture ('''Fig. 1b''').

[[Image:FIGURE_2_LYCOPENE.jpg]]

(Alper, 2005 - Permission Pending)

'''Figure 1''': The two measurements lycopene production in knockout strains of lycopene producing bacteria. ('''a''') A landscape displaying the 64 strains resulting for all possible combinations of gene knockouts selected through systematic modeling and directed evolution (combinatorial knockouts). Lycopene production for each strain was measured at the end of a 48-h shake-flask fermentation and amount of lycopene produced was quantified through extraction form the cell pellet with acetone and supernatant absorbance at 475 nm. Of interest is global maximum strain Δ''gdhA'' Δ''aceE'' Δ''PyjiD'', which contains a knockout of the Δ''PyjiD'' gene selected through directed evolution testing. ('''b''') Lycopene production of the best knockout strains in batch-fed culture. From left to right, the K12 strain from which combinatorial mutants were derived, the preengineered parental strain from which the systematically-selected knockout strains were derived, global maximum strain Δ''gdhA'' Δ''aceE'' Δ''fdhF'', global maximum strain Δ''gdhA'' Δ''aceE'' Δ''PyjiD'', and the global minimum strain and two local maximum strains from landscape 1a. Of interest is knockout strain Δ''gdhA'' Δ''aceE'' Δ''PyjiD'' (fourth from the left). This strain, which has a gene knockout selected through directed evolution testing, shows the same maximum in lycopene productivity as the entirely systematically-predicted strain Δ''gdhA'' Δ''aceE'' Δ''fdhF'' (third from left), but also shows an earlier peak in this productivity and more sustained lycopene production.

==Advantages of the Method==

# The researchers were able to improve an existing phenotype using a method of directed evolution which tested changes across the entire genome rather than in simply one gene.
# Unbiased selection found three genes to improve the studied phenotype which had not been predicted to optimize the lycopene production pathway in systematic modeling. Furthermore, two of these genes were previously undescribed.
# The gene knockouts selected through directed evolution in this experiment worked in concert with knockouts selected through systematic modeling to improve lycopene production in one of the best strains. This fact indicates the method is adaptable to use with other synthetically-engineered cells.

==Disadvantages of the Method==

# In this experiment, random transposon intergration could only test how gene knockouts increased lycopene production. The method of directed evolution described in this paper cannot test changes in gene expression levels or the genetic code itself to optimize phenotypes.
# While some randomly-selected knockouts were able to work with systematic knockouts to increase lycopene yield, others did not increase (and sometimes decreased) lycopene yields compared to the wild-type ('''Fig. 1a'''). Combining random knockouts and systematically predicted knockouts does not seem to have a 100% success rate.

==Source==

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Lycopene.pdf Alper, H, K. Miyaoku & G. Stephanopoulos. 2005. Construction of lycopene-overproducing ''E. coli'' strains by combining systematic and combinatorial gene knockout targets. ''Nature Biotechnology'' 23(5): 612-616.]

Random Gene Knockout to Maximize Product Yield

2007-12-10T17:11:35Z

WikiSysop: /* The Problem */

__NOTOC__

==The Experiment==

===The Problem===

Researchers Hal Alper, Kohei Miyaoku and Greg Stephanopoulos have recently demonstrated the effectiveness of using directed evolution to optimize a synthetic construct. Previously, the researchers engineered a strain of ''E. coli'' to synthesize lycopene, a carotenoid that naturally occurs in tomatoes. Lycopene is an antioxidant that has recently been incorporated into vitamin tablets, creating a commercial pressure to increase the efficiency and yield of lycopene synthesis.

===Computer Modeling to Increase Product Yield===

To further optimize lycopene production by the e. coli strain, the authors used computer modling to identify a series of gene knockouts sites that were predicted to increase lycopene production. However, the strains engineered to contain these knockouts were still unable to produced lycopene at the predicted “stoichiometric maximum.” This finding led the researchers to hypothesize that lycopene production was “limited by unknown kinetic or regulatory factors unaccounted for in the stoichiometric models.”

===Directed Evolution: Random Gene Knockout===

The researchers used directed evolution to find other gene knockouts that might affect these unexplored regions of the cell. A library of lycopene-producing E. coli with random gene knockout was achieved by introducing genome-wide integrating transposons to the cells in vivo. This random knockout library was then plated and tested for lycopene production. In this assay, lycopene production was proportional to the red color of bacterial colonies. The three best knockouts selected by this screening were there molecularly characterized. All three knockout sites were differed that had been predicted to be beneficial using their models. Two of these knockouts interrupted genes which had been previously undescribed. Thus, by using a random strategy, they discovered new genes whose expression affected the production of the desired product.

===Computer Modeling Versus Random Gene Knockout===

The team went on to determine the relative effectiveness of the three gene knockouts at increasing lycopene production when compared to the knockouts they had predicting with computer modeling. To answer this question, the researchers created 64 unique strains of the lycopene-producing E. coli representing all possible combinations of the three knockouts selected by directed evolution, the seven model-predicted knockouts, and the two parental strains from which the “evolved” and model-predicted strains were derived. Lycopene production of the 64 strains was measured by extracting the lycopene from the each colony after a defined interval of time and quantifying lycopene level by absorbance spectroscopy at 475. Of the two global maxima of this experiment, one had a knockout selected through directed evolution testing ('''Fig. 1a'''). Furthermore, this particular knockout strain also showed an earlier peak in lycopene production when compared to the completely systematically-predicted knockout strain in batch fed culture ('''Fig. 1b''').

[[Image:FIGURE_2_LYCOPENE.jpg]]

(Alper, 2005 - Permission Pending)

'''Figure 1''': The two measurements lycopene production in knockout strains of lycopene producing bacteria. ('''a''') A landscape displaying the 64 strains resulting for all possible combinations of gene knockouts selected through systematic modeling and directed evolution (combinatorial knockouts). Lycopene production for each strain was measured at the end of a 48-h shake-flask fermentation and amount of lycopene produced was quantified through extraction form the cell pellet with acetone and supernatant absorbance at 475 nm. Of interest is global maximum strain Δ''gdhA'' Δ''aceE'' Δ''PyjiD'', which contains a knockout of the Δ''PyjiD'' gene selected through directed evolution testing. ('''b''') Lycopene production of the best knockout strains in batch-fed culture. From left to right, the K12 strain from which combinatorial mutants were derived, the preengineered parental strain from which the systematically-selected knockout strains were derived, global maximum strain Δ''gdhA'' Δ''aceE'' Δ''fdhF'', global maximum strain Δ''gdhA'' Δ''aceE'' Δ''PyjiD'', and the global minimum strain and two local maximum strains from landscape 1a. Of interest is knockout strain Δ''gdhA'' Δ''aceE'' Δ''PyjiD'' (fourth from the left). This strain, which has a gene knockout selected through directed evolution testing, shows the same maximum in lycopene productivity as the entirely systematically-predicted strain Δ''gdhA'' Δ''aceE'' Δ''fdhF'' (third from left), but also shows an earlier peak in this productivity and more sustained lycopene production.

==Advantages of the Method==

# The researchers were able to improve an existing phenotype using a method of directed evolution which tested changes across the entire genome rather than in simply one gene.
# Unbiased selection found three genes to improve the studied phenotype which had not been predicted to optimize the lycopene production pathway in systematic modeling. Furthermore, two of these genes were previously undescribed.
# The gene knockouts selected through directed evolution in this experiment worked in concert with knockouts selected through systematic modeling to improve lycopene production in one of the best strains. This fact indicates the method is adaptable to use with other synthetically-engineered cells.

==Disadvantages of the Method==

# In this experiment, random transposon intergration could only test how gene knockouts increased lycopene production. The method of directed evolution described in this paper cannot test changes in gene expression levels or the genetic code itself to optimize phenotypes.
# While some randomly-selected knockouts were able to work with systematic knockouts to increase lycopene yield, others did not increase (and sometimes decreased) lycopene yields compared to the wild-type ('''Fig. 1a'''). Combining random knockouts and systematically predicted knockouts does not seem to have a 100% success rate.

==Source==

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Lycopene.pdf Alper, H, K. Miyaoku & G. Stephanopoulos. 2005. Construction of lycopene-overproducing ''E. coli'' strains by combining systematic and combinatorial gene knockout targets. ''Nature Biotechnology'' 23(5): 612-616.]

Directed Evolution and Synthetic Biology - Hunter Stone

2007-12-10T16:56:49Z

WikiSysop: /* Directed Evolution and Synthetic Biology */

====Project Proposal====

My project focuses on the use of random mutations to optimize synthetic pathways. Mathematical modeling and rational engineering of synthetic pathways is a powerful, proven tool to maximize product output. However, recently a series of unbiased strategies using recombinant methods have been shown to further increase product yield. These methods, which have been referred to as directed evolution, have produced powerful new methods and approaches for the synthetic biologist.

==Introduction - Pathway Optimization and Directed Evolution==

Researcher Jay Keasling has recently described a genetically-modified yeast strain that produces artemisinic acid, a chemical precursor to the antimalarial drug artemisinin (Ro, 2006). In these experiments, his team engineered yeast cells to express enzymes in a pathway that converts farnesyl pyrophosphate (FPP), a metabolic intermediate naturally occurring in yeast, into the desired product. Initially, however, this strain was unable to produce any appreciable amount of artemisin . Keasling’s team had run into a key problem facing many projects in synthetic biology: optimization. Although we are increasingly able to express sophisticated constructs within living cells, the existence of these frameworks does not always correspond with the ability to fulfill their intended purposes efficiently and effectively.

Keasling’s team chose to address this problem by rationally modifying the metabolism of their yeast strain. Although they were successful in increasing product yields, further optimization was required for them to meet their goals. What would be the best approach to increase product yield in this system? Were the changes the already made to the yeast’s metabolism truly the best for optimizing artemisinin output? Could changes in other distantly-related metabolic pathways have also helped to increase yields? Are there presently unknown elements in the cell affecting the new pathway which could potentially be changed? Are the enzymes in the new pathway themselves working at maximum efficiency?

One technique with the potential to answer all of these questions is directed evolution.

==Directed Evolution: The Method==

Directed evolution is a method used to create a more efficient mutant of an existing gene, RNA, pathway or cell. The method follows these general steps:

# A library of variants of the targeted construct (''e.g.'', a gene or a cell) is generated through random changes of its genomic DNA. Methods of genetic randomization include error-prone PCR, mutagenic agents like Mutazyme, or random transposon integration.
# The variant library goes through a process of screening or selection to reveal the most productive members of the library. Selection and screening techniques are specific to desired function of each experiment (''e.g.'' higher enzyme efficiency, greater cell resistance to ethanol).
# The most productive variant is resubmitted to the genetic randomization and selection processes.
# Steps 1-3 are repeated until the desired result is received - an evolved mutant more adept at the processes it was selected for than its unevolved parent.

[[Image:DIRECTEDEVOLUTION.jpg]]

==Directed Evolution and Synthetic Biology==

The power of directed evolution comes from two sources: its nonbiased nature and its ability to test changes in elements of the cell beyond present knowledge and understanding. The method has historically been used to maximize the function of a particular protein. New methods have been developed recently to maximize the function not just of a single protein, but of more complex phenotypes. Using directed evolution to improve both proteins and these more complex phenotypes like enzymatic pathways has tremendous promise for synthetic biology.

===Optimization of Enzyme Function===

Many projects in synthetic biology involve introducing foreign enzymatic pathways into microbes to produce a desired product. Examples include yeast cells engineered to produce atremisinin (Ro ''et al.'', 2006) or microbes engineered to produce fossil fuels ([http://www.amyrisbiotech.com/ Amyris], [http://www.ls9.com/ LS9]). The quantity of output from these pathways ultimately depends on the efficiency of the enzymes introduced. However, rational reengineering of these enzymes is an extremely difficult task due to the complexities of protein structure as well as the lack of sufficient knowledge regarding the relationship between protein structure and funtion.

Two papers describe successful use of directed evolution to improve product yield by augmenting enzymatic function. In both papers, the authors circumvent the laborious task of rational protein engineering by using directed evolution. In addition, the papers describe improvements in genetic randomization and selection to maximize enzyme function.

[[Semi-Synthetic DNA Shuffling and Doramectin]]

[[A Simple Method for Highly Evolved Enzymes]]

==="Genome-wide" Directed Evolution===

A second, emerging branch of directed evolution attempts to improve phenotypes regulated not just by an individual gene but by multiple genes across the entire genome.

This type of directed evolution provides a method to test changes in many different elements of a cell that make up a system. Attempts at directed evolution on such a scale are relatively new. The following papers describe the use of "genome-wide" directed evolution to improve product yield from complex pathways. So long as improvements can be screened and selected for, these methods might also be applied to improvement and optimization of complex synthetic phenotypes engineered by humans, such as cellular circuitry using an array of [[Logic Gates - Emma Garren|Logic Gates]].

[[Random Gene Knockout to Maximize Product Yield]]

[[Global Transcriptome Machinery Engineering]]

==Conclusion==

Researchers Stutzman-Engwall ''et al.'', Neuenschwander ''et al.'', and Alper ''et al''. have all described successful uses of a general method of engineering called directed evolution to improve desired phenotypes. At no point during these experiments did the researchers draw a model or plan specific changes to genetic code to engineer these phenotypes. Some might say that for this reason directed evolution does not belong within the realm of synythetic biology. At the same time, if synthetic biology is the “engineer’s approach to biology,” then what is an engineer ultimately concerned with except the production of a useful product that meets a need? The research described in the four papers reviewed certainly meets this criterion. Furthermore, the work by Stuzman-Engwall ''et al.'' and Neuenschwander ''et al.'' indicates that directed evolution is, in its own way, becoming an increasingly planned and rational process. Semi-synthetic DNA shuffling allows the researcher to which mutations stay in wheels of genetic randomization and selection, while the “selection vector” described in the work of Neuenschwander et al. allows the researcher to precisely control selective pressure on the enzyme being evolved.

Perhaps the best strategy is to leave the definitions aside. Alper’s team has shown in lycopene-producing ''E. coli'' that directed evolution can work in concert with traditonal synthetic modeling to meet overaching goals. The two methods appear well suited for one another. Directed evolution cannot be used to engineer new and creative permutations of enzymes like in Jay Keasling’s artemisinic acid-producing yeast; however, as shown in these four papers, the method is an effective way to improve existing systems at multiple levels. On the other hand, while synthetic biology can be used to construct complex biological systems, it is not always apparent to the synthetic biologist why what he or she has engineered does not meet optimal models. Directed evolution provides the synthetic biologist with a way of testing and improving entire systems in a nonbiased manner as they try to make synthetic constructs and optimal model agree.

As these four papers have shown, when directed evolution is applied to synthetic biology, the two methods work together to create interesting, new, and, most importantly, ''optimally-functioning'' pathways.

==Works Cited==

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Lycopene.pdf Alper, H, K. Miyaoku & G. Stephanopoulos. 2005. Construction of lycopene-overproducing ''E. coli'' strains by combining systematic and combinatorial gene knockout targets. ''Nature Biotechnology'' 23(5): 612-616.]

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Alper_etal.pdf Alper, H., J. Moxley, E. Nevoigt, G.R. Fink & G. Stephanopoulos. 2006. Engineering yeast transcription machinery for improved ethanol tolerance and production. ''Science'' 314: 1565-1568.]

[http://www.bio.davidson.edu/courses/synthetic/papers/highly_efficient_enzymes.pdf Neuenschwander, M., M. Butz, C. Heintz & D. Hilvert. 2007. A simple selection strategy for evolving highly efficient enzymes. ''Nature Biotechnology'' 25(10): 1145-1147.]

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Keasling_malaria.pdf Ro D, E.M. Paradise, M. Ouellet, K.J. Fisher, K.L. Newman, J.M. Ndungu, K.A. Ho, R.A. Eachus, T.S. Ham, J. Kirby, M.C.Y. Chang, S.T. Withers, Y. Shiba, R. Sarpong & J.D. Keasling. 2006. Production of the antimalarial drug precursor artemisinic acid in engineered yeast. ''Nature'' 440: 940-43.]

[http://www.pnas.org/cgi/reprint/91/22/10747 Stemmer, W.P.C. 1994. DNA shuffling by random fragmentation and reassembly: ''in vitro'' recombination for molecular evolution. ''PNAS'' 91: 10747-10751]

[http://www.bio.davidson.edu/courses/synthetic/papers/doramectin.pdf Stutzman-Engwall, K., S. Conlon, R. Fedechko, H. McArthur, K. Pekrun, Y. Chen, S. Jenne, C. La, N. Trinh, S. Kim, Y. Zhang, R. Fox, C. Gustafsson & A. Krebber. 2005. Semi-synthetic DNA shuffling of ''ave''C leads to improved industrial scale production of doramectin by ''Streptomyces avermitilis''. ''Metabolic Engineering'' 7: 27-37.]

Directed Evolution and Synthetic Biology - Hunter Stone

2007-12-10T16:56:25Z

WikiSysop: /* Directed Evolution and Synthetic Biology */

====Project Proposal====

My project focuses on the use of random mutations to optimize synthetic pathways. Mathematical modeling and rational engineering of synthetic pathways is a powerful, proven tool to maximize product output. However, recently a series of unbiased strategies using recombinant methods have been shown to further increase product yield. These methods, which have been referred to as directed evolution, have produced powerful new methods and approaches for the synthetic biologist.

==Introduction - Pathway Optimization and Directed Evolution==

Researcher Jay Keasling has recently described a genetically-modified yeast strain that produces artemisinic acid, a chemical precursor to the antimalarial drug artemisinin (Ro, 2006). In these experiments, his team engineered yeast cells to express enzymes in a pathway that converts farnesyl pyrophosphate (FPP), a metabolic intermediate naturally occurring in yeast, into the desired product. Initially, however, this strain was unable to produce any appreciable amount of artemisin . Keasling’s team had run into a key problem facing many projects in synthetic biology: optimization. Although we are increasingly able to express sophisticated constructs within living cells, the existence of these frameworks does not always correspond with the ability to fulfill their intended purposes efficiently and effectively.

Keasling’s team chose to address this problem by rationally modifying the metabolism of their yeast strain. Although they were successful in increasing product yields, further optimization was required for them to meet their goals. What would be the best approach to increase product yield in this system? Were the changes the already made to the yeast’s metabolism truly the best for optimizing artemisinin output? Could changes in other distantly-related metabolic pathways have also helped to increase yields? Are there presently unknown elements in the cell affecting the new pathway which could potentially be changed? Are the enzymes in the new pathway themselves working at maximum efficiency?

One technique with the potential to answer all of these questions is directed evolution.

==Directed Evolution: The Method==

Directed evolution is a method used to create a more efficient mutant of an existing gene, RNA, pathway or cell. The method follows these general steps:

# A library of variants of the targeted construct (''e.g.'', a gene or a cell) is generated through random changes of its genomic DNA. Methods of genetic randomization include error-prone PCR, mutagenic agents like Mutazyme, or random transposon integration.
# The variant library goes through a process of screening or selection to reveal the most productive members of the library. Selection and screening techniques are specific to desired function of each experiment (''e.g.'' higher enzyme efficiency, greater cell resistance to ethanol).
# The most productive variant is resubmitted to the genetic randomization and selection processes.
# Steps 1-3 are repeated until the desired result is received - an evolved mutant more adept at the processes it was selected for than its unevolved parent.

[[Image:DIRECTEDEVOLUTION.jpg]]

==Directed Evolution and Synthetic Biology==

The power of directed evolution comes from two sources: its nonbiased nature and its ability to test changes in elements of the cell beyond present knowledge and understanding. The method has historically been used to maximize the function of a particular protein. New methods have recently been developed to maximize the function not just of a single protein, but of more complex phenotypes. Using directed evolution to improve both proteins and these more complex phenotypes like enzymatic pathways has tremendous promise for synthetic biology.

===Optimization of Enzyme Function===

Many projects in synthetic biology involve introducing foreign enzymatic pathways into microbes to produce a desired product. Examples include yeast cells engineered to produce atremisinin (Ro ''et al.'', 2006) or microbes engineered to produce fossil fuels ([http://www.amyrisbiotech.com/ Amyris], [http://www.ls9.com/ LS9]). The quantity of output from these pathways ultimately depends on the efficiency of the enzymes introduced. However, rational reengineering of these enzymes is an extremely difficult task due to the complexities of protein structure as well as the lack of sufficient knowledge regarding the relationship between protein structure and funtion.

Two papers describe successful use of directed evolution to improve product yield by augmenting enzymatic function. In both papers, the authors circumvent the laborious task of rational protein engineering by using directed evolution. In addition, the papers describe improvements in genetic randomization and selection to maximize enzyme function.

[[Semi-Synthetic DNA Shuffling and Doramectin]]

[[A Simple Method for Highly Evolved Enzymes]]

==="Genome-wide" Directed Evolution===

A second, emerging branch of directed evolution attempts to improve phenotypes regulated not just by an individual gene but by multiple genes across the entire genome.

This type of directed evolution provides a method to test changes in many different elements of a cell that make up a system. Attempts at directed evolution on such a scale are relatively new. The following papers describe the use of "genome-wide" directed evolution to improve product yield from complex pathways. So long as improvements can be screened and selected for, these methods might also be applied to improvement and optimization of complex synthetic phenotypes engineered by humans, such as cellular circuitry using an array of [[Logic Gates - Emma Garren|Logic Gates]].

[[Random Gene Knockout to Maximize Product Yield]]

[[Global Transcriptome Machinery Engineering]]

==Conclusion==

Researchers Stutzman-Engwall ''et al.'', Neuenschwander ''et al.'', and Alper ''et al''. have all described successful uses of a general method of engineering called directed evolution to improve desired phenotypes. At no point during these experiments did the researchers draw a model or plan specific changes to genetic code to engineer these phenotypes. Some might say that for this reason directed evolution does not belong within the realm of synythetic biology. At the same time, if synthetic biology is the “engineer’s approach to biology,” then what is an engineer ultimately concerned with except the production of a useful product that meets a need? The research described in the four papers reviewed certainly meets this criterion. Furthermore, the work by Stuzman-Engwall ''et al.'' and Neuenschwander ''et al.'' indicates that directed evolution is, in its own way, becoming an increasingly planned and rational process. Semi-synthetic DNA shuffling allows the researcher to which mutations stay in wheels of genetic randomization and selection, while the “selection vector” described in the work of Neuenschwander et al. allows the researcher to precisely control selective pressure on the enzyme being evolved.

Perhaps the best strategy is to leave the definitions aside. Alper’s team has shown in lycopene-producing ''E. coli'' that directed evolution can work in concert with traditonal synthetic modeling to meet overaching goals. The two methods appear well suited for one another. Directed evolution cannot be used to engineer new and creative permutations of enzymes like in Jay Keasling’s artemisinic acid-producing yeast; however, as shown in these four papers, the method is an effective way to improve existing systems at multiple levels. On the other hand, while synthetic biology can be used to construct complex biological systems, it is not always apparent to the synthetic biologist why what he or she has engineered does not meet optimal models. Directed evolution provides the synthetic biologist with a way of testing and improving entire systems in a nonbiased manner as they try to make synthetic constructs and optimal model agree.

As these four papers have shown, when directed evolution is applied to synthetic biology, the two methods work together to create interesting, new, and, most importantly, ''optimally-functioning'' pathways.

==Works Cited==

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Lycopene.pdf Alper, H, K. Miyaoku & G. Stephanopoulos. 2005. Construction of lycopene-overproducing ''E. coli'' strains by combining systematic and combinatorial gene knockout targets. ''Nature Biotechnology'' 23(5): 612-616.]

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Alper_etal.pdf Alper, H., J. Moxley, E. Nevoigt, G.R. Fink & G. Stephanopoulos. 2006. Engineering yeast transcription machinery for improved ethanol tolerance and production. ''Science'' 314: 1565-1568.]

[http://www.bio.davidson.edu/courses/synthetic/papers/highly_efficient_enzymes.pdf Neuenschwander, M., M. Butz, C. Heintz & D. Hilvert. 2007. A simple selection strategy for evolving highly efficient enzymes. ''Nature Biotechnology'' 25(10): 1145-1147.]

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Keasling_malaria.pdf Ro D, E.M. Paradise, M. Ouellet, K.J. Fisher, K.L. Newman, J.M. Ndungu, K.A. Ho, R.A. Eachus, T.S. Ham, J. Kirby, M.C.Y. Chang, S.T. Withers, Y. Shiba, R. Sarpong & J.D. Keasling. 2006. Production of the antimalarial drug precursor artemisinic acid in engineered yeast. ''Nature'' 440: 940-43.]

[http://www.pnas.org/cgi/reprint/91/22/10747 Stemmer, W.P.C. 1994. DNA shuffling by random fragmentation and reassembly: ''in vitro'' recombination for molecular evolution. ''PNAS'' 91: 10747-10751]

[http://www.bio.davidson.edu/courses/synthetic/papers/doramectin.pdf Stutzman-Engwall, K., S. Conlon, R. Fedechko, H. McArthur, K. Pekrun, Y. Chen, S. Jenne, C. La, N. Trinh, S. Kim, Y. Zhang, R. Fox, C. Gustafsson & A. Krebber. 2005. Semi-synthetic DNA shuffling of ''ave''C leads to improved industrial scale production of doramectin by ''Streptomyces avermitilis''. ''Metabolic Engineering'' 7: 27-37.]

Directed Evolution and Synthetic Biology - Hunter Stone

2007-12-10T16:54:57Z

WikiSysop: /* Directed Evolution: The Method */

====Project Proposal====

My project focuses on the use of random mutations to optimize synthetic pathways. Mathematical modeling and rational engineering of synthetic pathways is a powerful, proven tool to maximize product output. However, recently a series of unbiased strategies using recombinant methods have been shown to further increase product yield. These methods, which have been referred to as directed evolution, have produced powerful new methods and approaches for the synthetic biologist.

==Introduction - Pathway Optimization and Directed Evolution==

Researcher Jay Keasling has recently described a genetically-modified yeast strain that produces artemisinic acid, a chemical precursor to the antimalarial drug artemisinin (Ro, 2006). In these experiments, his team engineered yeast cells to express enzymes in a pathway that converts farnesyl pyrophosphate (FPP), a metabolic intermediate naturally occurring in yeast, into the desired product. Initially, however, this strain was unable to produce any appreciable amount of artemisin . Keasling’s team had run into a key problem facing many projects in synthetic biology: optimization. Although we are increasingly able to express sophisticated constructs within living cells, the existence of these frameworks does not always correspond with the ability to fulfill their intended purposes efficiently and effectively.

Keasling’s team chose to address this problem by rationally modifying the metabolism of their yeast strain. Although they were successful in increasing product yields, further optimization was required for them to meet their goals. What would be the best approach to increase product yield in this system? Were the changes the already made to the yeast’s metabolism truly the best for optimizing artemisinin output? Could changes in other distantly-related metabolic pathways have also helped to increase yields? Are there presently unknown elements in the cell affecting the new pathway which could potentially be changed? Are the enzymes in the new pathway themselves working at maximum efficiency?

One technique with the potential to answer all of these questions is directed evolution.

==Directed Evolution: The Method==

Directed evolution is a method used to create a more efficient mutant of an existing gene, RNA, pathway or cell. The method follows these general steps:

# A library of variants of the targeted construct (''e.g.'', a gene or a cell) is generated through random changes of its genomic DNA. Methods of genetic randomization include error-prone PCR, mutagenic agents like Mutazyme, or random transposon integration.
# The variant library goes through a process of screening or selection to reveal the most productive members of the library. Selection and screening techniques are specific to desired function of each experiment (''e.g.'' higher enzyme efficiency, greater cell resistance to ethanol).
# The most productive variant is resubmitted to the genetic randomization and selection processes.
# Steps 1-3 are repeated until the desired result is received - an evolved mutant more adept at the processes it was selected for than its unevolved parent.

[[Image:DIRECTEDEVOLUTION.jpg]]

==Directed Evolution and Synthetic Biology==

The power of directed evolution comes from two sources: its nonbiased nature and its ability to test changes in elements of the cell beyond present knowledge and understanding. The method has historically been used to maximize the function of a particular protein . New methods have recently been developed to maximize the function not just of a single protein, but of more complex phenotypes. Using directed evolution to improve both proteins and these more complex phenotypes like enzymatic pathways has tremendous promise for synthetic biology.

===Optimization of Enzyme Function===

Many projects in synthetic biology involve introducing foreign enzymatic pathways into microbes to produce a desired product. Examples include yeast cells engineered to produce atremisinin (Ro ''et al.'', 2006) or microbes engineered to produce fossil fuels ([http://www.amyrisbiotech.com/ Amyris], [http://www.ls9.com/ LS9]). The quantity of output from these pathways ultimately depends on the efficiency of the enzymes introduced. However, rational reengineering of these enzymes is an extremely difficult task due to the complexities of protein structure as well as the lack of sufficient knowledge regarding the relationship between protein structure and funtion.

Two papers describe successful use of directed evolution to improve product yield by augmenting enzymatic function. In both papers, the authors circumvent the laborious task of rational protein engineering by using directed evolution. In addition, the papers describe improvements in genetic randomization and selection to maximize enzyme function.

[[Semi-Synthetic DNA Shuffling and Doramectin]]

[[A Simple Method for Highly Evolved Enzymes]]

==="Genome-wide" Directed Evolution===

A second, emerging branch of directed evolution attempts to improve phenotypes regulated not just by an individual gene but by multiple genes across the entire genome.

This type of directed evolution provides a method to test changes in many different elements of a cell that make up a system. Attempts at directed evolution on such a scale are relatively new. The following papers describe the use of "genome-wide" directed evolution to improve product yield from complex pathways. So long as improvements can be screened and selected for, these methods might also be applied to improvement and optimization of complex synthetic phenotypes engineered by humans, such as cellular circuitry using an array of [[Logic Gates - Emma Garren|Logic Gates]].

[[Random Gene Knockout to Maximize Product Yield]]

[[Global Transcriptome Machinery Engineering]]

==Conclusion==

Researchers Stutzman-Engwall ''et al.'', Neuenschwander ''et al.'', and Alper ''et al''. have all described successful uses of a general method of engineering called directed evolution to improve desired phenotypes. At no point during these experiments did the researchers draw a model or plan specific changes to genetic code to engineer these phenotypes. Some might say that for this reason directed evolution does not belong within the realm of synythetic biology. At the same time, if synthetic biology is the “engineer’s approach to biology,” then what is an engineer ultimately concerned with except the production of a useful product that meets a need? The research described in the four papers reviewed certainly meets this criterion. Furthermore, the work by Stuzman-Engwall ''et al.'' and Neuenschwander ''et al.'' indicates that directed evolution is, in its own way, becoming an increasingly planned and rational process. Semi-synthetic DNA shuffling allows the researcher to which mutations stay in wheels of genetic randomization and selection, while the “selection vector” described in the work of Neuenschwander et al. allows the researcher to precisely control selective pressure on the enzyme being evolved.

Perhaps the best strategy is to leave the definitions aside. Alper’s team has shown in lycopene-producing ''E. coli'' that directed evolution can work in concert with traditonal synthetic modeling to meet overaching goals. The two methods appear well suited for one another. Directed evolution cannot be used to engineer new and creative permutations of enzymes like in Jay Keasling’s artemisinic acid-producing yeast; however, as shown in these four papers, the method is an effective way to improve existing systems at multiple levels. On the other hand, while synthetic biology can be used to construct complex biological systems, it is not always apparent to the synthetic biologist why what he or she has engineered does not meet optimal models. Directed evolution provides the synthetic biologist with a way of testing and improving entire systems in a nonbiased manner as they try to make synthetic constructs and optimal model agree.

As these four papers have shown, when directed evolution is applied to synthetic biology, the two methods work together to create interesting, new, and, most importantly, ''optimally-functioning'' pathways.

==Works Cited==

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Lycopene.pdf Alper, H, K. Miyaoku & G. Stephanopoulos. 2005. Construction of lycopene-overproducing ''E. coli'' strains by combining systematic and combinatorial gene knockout targets. ''Nature Biotechnology'' 23(5): 612-616.]

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Alper_etal.pdf Alper, H., J. Moxley, E. Nevoigt, G.R. Fink & G. Stephanopoulos. 2006. Engineering yeast transcription machinery for improved ethanol tolerance and production. ''Science'' 314: 1565-1568.]

[http://www.bio.davidson.edu/courses/synthetic/papers/highly_efficient_enzymes.pdf Neuenschwander, M., M. Butz, C. Heintz & D. Hilvert. 2007. A simple selection strategy for evolving highly efficient enzymes. ''Nature Biotechnology'' 25(10): 1145-1147.]

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Keasling_malaria.pdf Ro D, E.M. Paradise, M. Ouellet, K.J. Fisher, K.L. Newman, J.M. Ndungu, K.A. Ho, R.A. Eachus, T.S. Ham, J. Kirby, M.C.Y. Chang, S.T. Withers, Y. Shiba, R. Sarpong & J.D. Keasling. 2006. Production of the antimalarial drug precursor artemisinic acid in engineered yeast. ''Nature'' 440: 940-43.]

[http://www.pnas.org/cgi/reprint/91/22/10747 Stemmer, W.P.C. 1994. DNA shuffling by random fragmentation and reassembly: ''in vitro'' recombination for molecular evolution. ''PNAS'' 91: 10747-10751]

[http://www.bio.davidson.edu/courses/synthetic/papers/doramectin.pdf Stutzman-Engwall, K., S. Conlon, R. Fedechko, H. McArthur, K. Pekrun, Y. Chen, S. Jenne, C. La, N. Trinh, S. Kim, Y. Zhang, R. Fox, C. Gustafsson & A. Krebber. 2005. Semi-synthetic DNA shuffling of ''ave''C leads to improved industrial scale production of doramectin by ''Streptomyces avermitilis''. ''Metabolic Engineering'' 7: 27-37.]

Directed Evolution and Synthetic Biology - Hunter Stone

2007-12-10T16:54:35Z

WikiSysop: /* Directed Evolution: The Method */

====Project Proposal====

My project focuses on the use of random mutations to optimize synthetic pathways. Mathematical modeling and rational engineering of synthetic pathways is a powerful, proven tool to maximize product output. However, recently a series of unbiased strategies using recombinant methods have been shown to further increase product yield. These methods, which have been referred to as directed evolution, have produced powerful new methods and approaches for the synthetic biologist.

==Introduction - Pathway Optimization and Directed Evolution==

Researcher Jay Keasling has recently described a genetically-modified yeast strain that produces artemisinic acid, a chemical precursor to the antimalarial drug artemisinin (Ro, 2006). In these experiments, his team engineered yeast cells to express enzymes in a pathway that converts farnesyl pyrophosphate (FPP), a metabolic intermediate naturally occurring in yeast, into the desired product. Initially, however, this strain was unable to produce any appreciable amount of artemisin . Keasling’s team had run into a key problem facing many projects in synthetic biology: optimization. Although we are increasingly able to express sophisticated constructs within living cells, the existence of these frameworks does not always correspond with the ability to fulfill their intended purposes efficiently and effectively.

Keasling’s team chose to address this problem by rationally modifying the metabolism of their yeast strain. Although they were successful in increasing product yields, further optimization was required for them to meet their goals. What would be the best approach to increase product yield in this system? Were the changes the already made to the yeast’s metabolism truly the best for optimizing artemisinin output? Could changes in other distantly-related metabolic pathways have also helped to increase yields? Are there presently unknown elements in the cell affecting the new pathway which could potentially be changed? Are the enzymes in the new pathway themselves working at maximum efficiency?

One technique with the potential to answer all of these questions is directed evolution.

==Directed Evolution: The Method==

Directed evolution is a method used to create a more efficient mutant of an existing gene, RNA, pathway or cell. The method follows these general steps:

# A library of variants of the targeted construct (''e.g.'', a gene or a cell) is generated through random changes of its genomic DNA. Methods of genetic randomization include error-prone PCR, mutagenic agents like Mutazyme, or random transposon integration.
# The variant library goes through a process of screening or selection to reveal the most productive members of the library. Selection and screening techniques are specific to desired function of each experiment (e.g. higher enzyme efficiency, greater cell resistance to ethanol).
# The most productive variant is resubmitted to the genetic randomization and selection processes.
# Steps 1-3 are repeated until the desired result is received - an evolved mutant more adept at the processes it was selected for than its unevolved parent.

[[Image:DIRECTEDEVOLUTION.jpg]]

==Directed Evolution and Synthetic Biology==

The power of directed evolution comes from two sources: its nonbiased nature and its ability to test changes in elements of the cell beyond present knowledge and understanding. The method has historically been used to maximize the function of a particular protein . New methods have recently been developed to maximize the function not just of a single protein, but of more complex phenotypes. Using directed evolution to improve both proteins and these more complex phenotypes like enzymatic pathways has tremendous promise for synthetic biology.

===Optimization of Enzyme Function===

Many projects in synthetic biology involve introducing foreign enzymatic pathways into microbes to produce a desired product. Examples include yeast cells engineered to produce atremisinin (Ro ''et al.'', 2006) or microbes engineered to produce fossil fuels ([http://www.amyrisbiotech.com/ Amyris], [http://www.ls9.com/ LS9]). The quantity of output from these pathways ultimately depends on the efficiency of the enzymes introduced. However, rational reengineering of these enzymes is an extremely difficult task due to the complexities of protein structure as well as the lack of sufficient knowledge regarding the relationship between protein structure and funtion.

Two papers describe successful use of directed evolution to improve product yield by augmenting enzymatic function. In both papers, the authors circumvent the laborious task of rational protein engineering by using directed evolution. In addition, the papers describe improvements in genetic randomization and selection to maximize enzyme function.

[[Semi-Synthetic DNA Shuffling and Doramectin]]

[[A Simple Method for Highly Evolved Enzymes]]

==="Genome-wide" Directed Evolution===

A second, emerging branch of directed evolution attempts to improve phenotypes regulated not just by an individual gene but by multiple genes across the entire genome.

This type of directed evolution provides a method to test changes in many different elements of a cell that make up a system. Attempts at directed evolution on such a scale are relatively new. The following papers describe the use of "genome-wide" directed evolution to improve product yield from complex pathways. So long as improvements can be screened and selected for, these methods might also be applied to improvement and optimization of complex synthetic phenotypes engineered by humans, such as cellular circuitry using an array of [[Logic Gates - Emma Garren|Logic Gates]].

[[Random Gene Knockout to Maximize Product Yield]]

[[Global Transcriptome Machinery Engineering]]

==Conclusion==

Researchers Stutzman-Engwall ''et al.'', Neuenschwander ''et al.'', and Alper ''et al''. have all described successful uses of a general method of engineering called directed evolution to improve desired phenotypes. At no point during these experiments did the researchers draw a model or plan specific changes to genetic code to engineer these phenotypes. Some might say that for this reason directed evolution does not belong within the realm of synythetic biology. At the same time, if synthetic biology is the “engineer’s approach to biology,” then what is an engineer ultimately concerned with except the production of a useful product that meets a need? The research described in the four papers reviewed certainly meets this criterion. Furthermore, the work by Stuzman-Engwall ''et al.'' and Neuenschwander ''et al.'' indicates that directed evolution is, in its own way, becoming an increasingly planned and rational process. Semi-synthetic DNA shuffling allows the researcher to which mutations stay in wheels of genetic randomization and selection, while the “selection vector” described in the work of Neuenschwander et al. allows the researcher to precisely control selective pressure on the enzyme being evolved.

Perhaps the best strategy is to leave the definitions aside. Alper’s team has shown in lycopene-producing ''E. coli'' that directed evolution can work in concert with traditonal synthetic modeling to meet overaching goals. The two methods appear well suited for one another. Directed evolution cannot be used to engineer new and creative permutations of enzymes like in Jay Keasling’s artemisinic acid-producing yeast; however, as shown in these four papers, the method is an effective way to improve existing systems at multiple levels. On the other hand, while synthetic biology can be used to construct complex biological systems, it is not always apparent to the synthetic biologist why what he or she has engineered does not meet optimal models. Directed evolution provides the synthetic biologist with a way of testing and improving entire systems in a nonbiased manner as they try to make synthetic constructs and optimal model agree.

As these four papers have shown, when directed evolution is applied to synthetic biology, the two methods work together to create interesting, new, and, most importantly, ''optimally-functioning'' pathways.

==Works Cited==

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Lycopene.pdf Alper, H, K. Miyaoku & G. Stephanopoulos. 2005. Construction of lycopene-overproducing ''E. coli'' strains by combining systematic and combinatorial gene knockout targets. ''Nature Biotechnology'' 23(5): 612-616.]

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Alper_etal.pdf Alper, H., J. Moxley, E. Nevoigt, G.R. Fink & G. Stephanopoulos. 2006. Engineering yeast transcription machinery for improved ethanol tolerance and production. ''Science'' 314: 1565-1568.]

[http://www.bio.davidson.edu/courses/synthetic/papers/highly_efficient_enzymes.pdf Neuenschwander, M., M. Butz, C. Heintz & D. Hilvert. 2007. A simple selection strategy for evolving highly efficient enzymes. ''Nature Biotechnology'' 25(10): 1145-1147.]

[http://www.bio.davidson.edu/Courses/Synthetic/papers/Keasling_malaria.pdf Ro D, E.M. Paradise, M. Ouellet, K.J. Fisher, K.L. Newman, J.M. Ndungu, K.A. Ho, R.A. Eachus, T.S. Ham, J. Kirby, M.C.Y. Chang, S.T. Withers, Y. Shiba, R. Sarpong & J.D. Keasling. 2006. Production of the antimalarial drug precursor artemisinic acid in engineered yeast. ''Nature'' 440: 940-43.]

[http://www.pnas.org/cgi/reprint/91/22/10747 Stemmer, W.P.C. 1994. DNA shuffling by random fragmentation and reassembly: ''in vitro'' recombination for molecular evolution. ''PNAS'' 91: 10747-10751]

[http://www.bio.davidson.edu/courses/synthetic/papers/doramectin.pdf Stutzman-Engwall, K., S. Conlon, R. Fedechko, H. McArthur, K. Pekrun, Y. Chen, S. Jenne, C. La, N. Trinh, S. Kim, Y. Zhang, R. Fox, C. Gustafsson & A. Krebber. 2005. Semi-synthetic DNA shuffling of ''ave''C leads to improved industrial scale production of doramectin by ''Streptomyces avermitilis''. ''Metabolic Engineering'' 7: 27-37.]

Modeling Promoter Activity

2007-12-10T16:46:44Z

WikiSysop: /* Jensen, Alper, Fischer, and Stephanopoulis (2006): Statistical Modeling and Critical Mutation Sites */

== Modeling Promoter Activity ==

In order to use synthetic promoters to their fullest potential, we have to understand how they work. Sythetic promoters cannot help us model gene circuit activity unless models are developed for the activity of the promoter itself. Determining how exactly a promoter's strength correlates to its mutations is not easy, since for the most part it requires working with promoters on the level of individual sets of nucleiotides.

=== Jensen and Hammer (1997): Spacer Sequences ===

In this 1997 paper, Jensen and Hammer constructed a library of synthetic promoters based on the ''Lactococcus lactis'' prokaryotic promoter in order to better determine how gene sequence of promoters was tied to the promoter strength. Specifically, Jensen and Hammer were looking for a way to construct a constitutively active promoter – one that was always turned on, without needing an inducer – that could be safely used to tune gene expression in industrial-scale metabolic engineering projects, where inducers might be impractical or hazardous.

In order to tune the steady-state ''L. lactis'' promoter without using an inducer, Jensen and Hammer had to create a library of ''L. lactis'' mutant promoters, all with various levels of activity. To generate the library, they used the method described in [[Promoters and Reporters in Synthetic Biology]]: constructing oligonucleiotides that matched the genes common to all previous ''L. lactis'' promoters and mutants, then allowing the oligonucleiotides to be joined together by random spacer sequences.

After the promoter library was synthesized, promoters were cloned into both ''L. lactis'' and ''E. coli''; each cell culture containing a different promoter was tested for the level of beta-galactosidase activity. The activity of each promoter (in Miller units, or beta-galactosidase concentration) is described in Figure 3.

[[Image:Am0180933003.gif]] 
Figure 3. Library of synthetic promoters for L. lactis. Promoter activities (Miller units) were assayed from the expression of a reporter gene (lacLM) encoding -galactosidase transcribed from the different synthetic promoter clones on the promoter cloning vector pAK80. The patterns of the data points indicate which promoter clones contain errors in either the 35 or the 10 consensus sequence or in the length of the spacer between these sequences. From Jensen and Hammer (1997). Permission Pending.

The mutant promoters expressed a wide range of activity, increasing in small increments. Note that not all of the clones were "perfect" - a few had mutations in the oligonucleotide sequences that were supposed to be preserved across the library. Those clones are indicated in the graph above. However, their data was not removed because it was within range of the data from the perfect clones - they caused no break in the general data trend. In addition, all clones were tested to ensure that they were truly constitutive.

When the promoters were cloned into ''E. coli'', the same basic trend was observed. While the promoters did not demonstrate the same level of activity as they did in ''L. lactis'', there was still a wide range of activity observed, with the activity level increasing in steady increments.

Jensen and Hammer constructed a library of synthetic promoters that could be constitutively expressed and covered a range of activity levels, but it was still not known for certain what caused a certain promoter to be active at a certain rate. Jensen and Hammer suggested in their Discussion that "it seems that the overall three-dimensional structure which arises from a particular nucleiotide sequence could be important".

=== Jensen, Alper, Fischer, and Stephanopoulis (2006): Statistical Modeling and Critical Mutation Sites ===

In this paper, Jensen ''et al''. tried to determine exactly why some promoters in a promoter library were stronger than others, and which mutations might cause the change in strength. Jensen ''et al''. propose to examine promoter libraries statistically rather than via assays; they will determine which mutations are associated with which phenotypes based on when they appear.

Imagine you are creating a mutant library of a protein that can fluoresce one of three colors: red, blue, or green. If a given point mutation – let’s call it A – has no effect on the color of the fluorescence, then (assuming the mutagenesis is truly random) that mutation should appear in every phenotype proportional to the amount of protein with that phenotype. It will not appear in one phenotype significantly more than the others unless there is significantly more protein with that phenotype. It follows, then, that if point mutation B appears much more often in, say, blue protein ''without there being much more blue protein than red or green protein'', mutation B might have some effect on the protein’s phenotype. It is probably not the sole cause of the blue color, but it is associated with it.

To test their statistical analysis, Jensen et al generated different variants of a single promoter via error-prone PCR, fused the promoter into a plasmid with a GFP reporter gene, and then measured the amount of GFP via flow cytometry. The promoters were then sequenced, and any with insertions or deletions were removed until 69 promoters remained.

Now, assume that each mutant can be classified into one of an unknown number or phenotypic (descriptive) classes; let's call that number M. So there would be n(m) mutants in each class, with the summation of n(m) equalling all hypothetical mutants. Now, say you have a set of mutated promoters of size X, where X < N, all with one particular point mutation. If that mutation has no effect on the phenotype of the promoter, then the number of mutants in any given class with that point mutation would equal X/N - the total number of those mutants divided by the total number of promoters. In other words, they would be distributed evenly.

In multinomial statiestics, the probability that any one set X will take on another set of values y is:

[[Image:Fd2_1.gif]]

Where the summation of y is equal to X. Given that summation, the probability that q or more of any specific mutant appearing in a particular class (P(i)) is:

[[Image:Fd5_4.gif]]

The 69 promoters being examined were divided into two phenotypic classes based on their fluorescence: the top 50th percentile (brightest) and the bottom 50th percentile (dimmest). Because there are only two classes, the statistical analysis is simplified somewhat. The complete statistical analysis can be seen here in Figure 5:

[[Image:Zam0050667180002.gif]]

Figure 5. Statistical distribution of mutations and their effects on mutant fluorescence. In panel A, the vertical axis shows the mutant number, where the mutants are sorted in descending order by their relative fluorescence. In general, the single-cell fluorescence distribution for each mutant strain was log normal distributed. The horizontal axis shows the mean of the log relative fluorescence for each mutant strain, where the error is the standard deviation of this distribution. Reading to the right from panel A into panel B reveals the point mutations present in each mutant. For each location in a mutant (where location is indicated on the horizontal axis) that was changed via the error-prone PCR, a black dot is indicated. With only two exceptions, all of these changes are base transitions rather than transversions, so the sequence of each of the 69 clones can be inferred from the wild-type sequence shown in panel D. (All of the mutations indicated in panel B are transitions with the exception of one A-C transversion at –125 bp in clone 53 and one T-G transversion at –8 in clone 68. These were treated as though they were transitions in our analysis.) Reading down from panel B into panel C shows how mutations at a particular location partition between the two classes of mutants: the top and bottom 50th percentiles. Sites that have no effect on the fluorescence phenotype should partition equally between the two classes, i.e., they should follow a binomial distribution with P = 0.5. Sites that deviate from this distribution are labeled with a dot and are colored either green or red, corresponding to the apparent effect of a mutation at the site. For these sites, P values are indicated, where this value is the probability of seeing a distribution at least as skewed to one side. Sites that were subsequently tested experimentally (see text) are indicated with an asterisk, where the color of the asterisk denotes the expected effect of a mutation at the site. We chose a range of sites to test experimentally from sites with high-confidence (low P value) positive effects to those with low-confidence (P value 0.5) negative effects (Table 1). These sites are also shown in panel D, which contains the wild-type nucleotide sequence of the promoter region that was subjected to mutation. From Jensen ''et al''. (2006). Permission pending.

Statistical analysis revealed seven nucleiotide positions that were correlated with one of the two classes in a significant manner. These seven positions were then tested individually, to see if their phenotype when isolated matched their phenotype when the mutation was random (and accompanied by many other mutations).

When tested, six out of the seven mutants proved to have a similar phenotype in isolation to the phenotype they had in the random mutations, meaning that the statistical model used to predict the significant mutations was accurate and predicted correctly.

=== De Mey, Maertens, Lequeux, Soetart, and Vandamme (2007): Probability and Partial Least Squares Modeling ===

Modeling Promoter Activity

2007-12-10T16:43:55Z

WikiSysop: /* Jensen, Alper, Fischer, and Stephanopoulis (2006): Statistical Modeling and Critical Mutation Sites */

== Modeling Promoter Activity ==

In order to use synthetic promoters to their fullest potential, we have to understand how they work. Sythetic promoters cannot help us model gene circuit activity unless models are developed for the activity of the promoter itself. Determining how exactly a promoter's strength correlates to its mutations is not easy, since for the most part it requires working with promoters on the level of individual sets of nucleiotides.

=== Jensen and Hammer (1997): Spacer Sequences ===

In this 1997 paper, Jensen and Hammer constructed a library of synthetic promoters based on the ''Lactococcus lactis'' prokaryotic promoter in order to better determine how gene sequence of promoters was tied to the promoter strength. Specifically, Jensen and Hammer were looking for a way to construct a constitutively active promoter – one that was always turned on, without needing an inducer – that could be safely used to tune gene expression in industrial-scale metabolic engineering projects, where inducers might be impractical or hazardous.

In order to tune the steady-state ''L. lactis'' promoter without using an inducer, Jensen and Hammer had to create a library of ''L. lactis'' mutant promoters, all with various levels of activity. To generate the library, they used the method described in [[Promoters and Reporters in Synthetic Biology]]: constructing oligonucleiotides that matched the genes common to all previous ''L. lactis'' promoters and mutants, then allowing the oligonucleiotides to be joined together by random spacer sequences.

After the promoter library was synthesized, promoters were cloned into both ''L. lactis'' and ''E. coli''; each cell culture containing a different promoter was tested for the level of beta-galactosidase activity. The activity of each promoter (in Miller units, or beta-galactosidase concentration) is described in Figure 3.

[[Image:Am0180933003.gif]] 
Figure 3. Library of synthetic promoters for L. lactis. Promoter activities (Miller units) were assayed from the expression of a reporter gene (lacLM) encoding -galactosidase transcribed from the different synthetic promoter clones on the promoter cloning vector pAK80. The patterns of the data points indicate which promoter clones contain errors in either the 35 or the 10 consensus sequence or in the length of the spacer between these sequences. From Jensen and Hammer (1997). Permission Pending.

The mutant promoters expressed a wide range of activity, increasing in small increments. Note that not all of the clones were "perfect" - a few had mutations in the oligonucleotide sequences that were supposed to be preserved across the library. Those clones are indicated in the graph above. However, their data was not removed because it was within range of the data from the perfect clones - they caused no break in the general data trend. In addition, all clones were tested to ensure that they were truly constitutive.

When the promoters were cloned into ''E. coli'', the same basic trend was observed. While the promoters did not demonstrate the same level of activity as they did in ''L. lactis'', there was still a wide range of activity observed, with the activity level increasing in steady increments.

Jensen and Hammer constructed a library of synthetic promoters that could be constitutively expressed and covered a range of activity levels, but it was still not known for certain what caused a certain promoter to be active at a certain rate. Jensen and Hammer suggested in their Discussion that "it seems that the overall three-dimensional structure which arises from a particular nucleiotide sequence could be important".

=== Jensen, Alper, Fischer, and Stephanopoulis (2006): Statistical Modeling and Critical Mutation Sites ===

In this paper, Jensen ''et al''. tried to determine exactly why some promoters in a promoter library were stronger than others, and which mutations might cause the change in strength. Jensen ''et al''. propose to examine promoter libraries statistically rather than via assays; they will determine which mutations are associated with which phenotypes based on when they appear.

Imagine you are creating a mutant library of a protein that can fluoresce one of three colors: red, blue, or green. If a given point mutation – let’s call it A – has no effect on the color of the fluorescence, then (assuming the mutagenesis is truly random) that mutation should appear in every phenotype proportional to the amount of protein with that phenotype. It will not appear in one phenotype significantly more than the others unless there is significantly more protein with that phenotype. It follows, then, that if point mutation B appears much more often in, say, blue protein ''without there being much more blue protein than red or green protein'', mutation B might have some effect on the protein’s phenotype. It is probably not the sole cause of the blue color, but it is associated with it.

To test their statistical analysis, Jensen et al generated different variants of a single promoter via error-prone PCR, fused the promoter into a plasmid with a GFP reporter gene, and then measured the amount of GFP via flow cytometry. The promoters were then sequenced, and any with insertions or deletions were removed until 69 promoters remained.

Now, assume that each mutant can be classified into one of an unknown number or phenotypic (descriptive) classes; let's call that number M. So there would be n(m) mutants in each class, with the summation of n(m) equalling all hypothetical mutants. Now, say you have a set of mutated promoters of size X, where X < N, all with one particular point mutation. If that mutation has no effect on the phenotype of the promoter, then the number of mutants in any given class with that point mutation would equal X/N - the total number of those mutants divided by the total number of promoters. In other words, they would be distributed evenly.

In multinomial statiestics, the probability that any one set X will take on another set of values y is:

[[Image:Fd2_1.gif]]

Where the summation of y is equal to X. Given that summation, the probability that q or more of any specific mutant appearing in a particular class (P(i)) is:

[[Image:Fd5_4.gif]]

The 69 promoters being examined were divided into two phenotypic classes based on their fluorescence: the top 50th percentile (brightest) and the bottom 50th percentile (dimmest). Because there are only two classes, the statistical analysis is simplified somewhat. The complete statistical analysis can be seen here in Figure 5:

[[Image:Zam0050667180002.gif]]

Figure 5. Statistical distribution of mutations and their effects on mutant fluorescence. In panel A, the vertical axis shows the mutant number, where the mutants are sorted in descending order by their relative fluorescence. In general, the single-cell fluorescence distribution for each mutant strain was log normal distributed. The horizontal axis shows the mean of the log relative fluorescence for each mutant strain, where the error is the standard deviation of this distribution. Reading to the right from panel A into panel B reveals the point mutations present in each mutant. For each location in a mutant (where location is indicated on the horizontal axis) that was changed via the error-prone PCR, a black dot is indicated. With only two exceptions, all of these changes are base transitions rather than transversions, so the sequence of each of the 69 clones can be inferred from the wild-type sequence shown in panel D. (All of the mutations indicated in panel B are transitions with the exception of one A-C transversion at –125 bp in clone 53 and one T-G transversion at –8 in clone 68. These were treated as though they were transitions in our analysis.) Reading down from panel B into panel C shows how mutations at a particular location partition between the two classes of mutants: the top and bottom 50th percentiles. Sites that have no effect on the fluorescence phenotype should partition equally between the two classes, i.e., they should follow a binomial distribution with P = 0.5. Sites that deviate from this distribution are labeled with a dot and are colored either green or red, corresponding to the apparent effect of a mutation at the site. For these sites, P values are indicated, where this value is the probability of seeing a distribution at least as skewed to one side. Sites that were subsequently tested experimentally (see text) are indicated with an asterisk, where the color of the asterisk denotes the expected effect of a mutation at the site. We chose a range of sites to test experimentally from sites with high-confidence (low P value) positive effects to those with low-confidence (P value 0.5) negative effects (Table 1). These sites are also shown in panel D, which contains the wild-type nucleotide sequence of the promoter region that was subjected to mutation. From Jensen et all (2006). Permission pending.

Statistical analysis revealed seven nucleiotide positions that were correlated with one of the two classes in a significant manner. These seven positions were then tested individually, to see if their phenotype when isolated matched their phenotype when the mutation was random (and accompanied by many other mutations).

When tested, six out of the seven mutants proved to have a similar phenotype in isolation to the phenotype they had in the random mutations, meaning that the statistical model used to predict the significant mutations was accurate and predicted correctly.

=== De Mey, Maertens, Lequeux, Soetart, and Vandamme (2007): Probability and Partial Least Squares Modeling ===

Modeling Promoter Activity

2007-12-10T16:42:45Z

WikiSysop: /* Jensen, Alper, Fischer, and Stephanopoulis (2006): Statistical Modeling and Critical Mutation Sites */

== Modeling Promoter Activity ==

In order to use synthetic promoters to their fullest potential, we have to understand how they work. Sythetic promoters cannot help us model gene circuit activity unless models are developed for the activity of the promoter itself. Determining how exactly a promoter's strength correlates to its mutations is not easy, since for the most part it requires working with promoters on the level of individual sets of nucleiotides.

=== Jensen and Hammer (1997): Spacer Sequences ===

In this 1997 paper, Jensen and Hammer constructed a library of synthetic promoters based on the ''Lactococcus lactis'' prokaryotic promoter in order to better determine how gene sequence of promoters was tied to the promoter strength. Specifically, Jensen and Hammer were looking for a way to construct a constitutively active promoter – one that was always turned on, without needing an inducer – that could be safely used to tune gene expression in industrial-scale metabolic engineering projects, where inducers might be impractical or hazardous.

In order to tune the steady-state ''L. lactis'' promoter without using an inducer, Jensen and Hammer had to create a library of ''L. lactis'' mutant promoters, all with various levels of activity. To generate the library, they used the method described in [[Promoters and Reporters in Synthetic Biology]]: constructing oligonucleiotides that matched the genes common to all previous ''L. lactis'' promoters and mutants, then allowing the oligonucleiotides to be joined together by random spacer sequences.

After the promoter library was synthesized, promoters were cloned into both ''L. lactis'' and ''E. coli''; each cell culture containing a different promoter was tested for the level of beta-galactosidase activity. The activity of each promoter (in Miller units, or beta-galactosidase concentration) is described in Figure 3.

[[Image:Am0180933003.gif]] 
Figure 3. Library of synthetic promoters for L. lactis. Promoter activities (Miller units) were assayed from the expression of a reporter gene (lacLM) encoding -galactosidase transcribed from the different synthetic promoter clones on the promoter cloning vector pAK80. The patterns of the data points indicate which promoter clones contain errors in either the 35 or the 10 consensus sequence or in the length of the spacer between these sequences. From Jensen and Hammer (1997). Permission Pending.

The mutant promoters expressed a wide range of activity, increasing in small increments. Note that not all of the clones were "perfect" - a few had mutations in the oligonucleotide sequences that were supposed to be preserved across the library. Those clones are indicated in the graph above. However, their data was not removed because it was within range of the data from the perfect clones - they caused no break in the general data trend. In addition, all clones were tested to ensure that they were truly constitutive.

When the promoters were cloned into ''E. coli'', the same basic trend was observed. While the promoters did not demonstrate the same level of activity as they did in ''L. lactis'', there was still a wide range of activity observed, with the activity level increasing in steady increments.

Jensen and Hammer constructed a library of synthetic promoters that could be constitutively expressed and covered a range of activity levels, but it was still not known for certain what caused a certain promoter to be active at a certain rate. Jensen and Hammer suggested in their Discussion that "it seems that the overall three-dimensional structure which arises from a particular nucleiotide sequence could be important".

=== Jensen, Alper, Fischer, and Stephanopoulis (2006): Statistical Modeling and Critical Mutation Sites ===

In this paper, Jensen ''et al''. tried to determine exactly why some promoters in a promoter library were stronger than others, and which mutations might cause the change in strength. Jensen et al propose to examine promoter libraries statistically rather than via assays; they will determine which mutations are associated with which phenotypes based on when they appear.

Say, for example, that you are creating a mutant library of a protein that can fluoresce one of three colors: red, blue, or green. If a given point mutation – let’s call it A – has no effect on the color of the fluorescence, then (assuming the mutagenesis is truly random) that mutation should appear in every phenotype proportional to the amount of protein with that phenotype. It will not appear in one phenotype significantly more than the others unless there is significantly more protein with that phenotype. It follows, then, that if point mutation B appears much more often in, say, blue protein ''without there being much more blue protein than red or green protein'', mutation B might have some effect on the protein’s phenotype. It is probably not the sole cause of the blue color, but it is associated with it.

To test their statistical analysis, Jensen et al generated different variants of a single promoter via error-prone PCR, fused the promoter into a plasmid with a GFP reporter gene, and then measured the amount of GFP via flow cytometry. The promoters were then sequenced, and any with insertions or deletions were removed until 69 promoters remained.

Now, assume that each mutant can be classified into one of an unknown number or phenotypic (descriptive) classes; let's call that number M. So there would be n(m) mutants in each class, with the summation of n(m) equalling all hypothetical mutants. Now, say you have a set of mutated promoters of size X, where X < N, all with one particular point mutation. If that mutation has no effect on the phenotype of the promoter, then the number of mutants in any given class with that point mutation would equal X/N - the total number of those mutants divided by the total number of promoters. In other words, they would be distributed evenly.

In multinomial statiestics, the probability that any one set X will take on another set of values y is:

[[Image:Fd2_1.gif]]

Where the summation of y is equal to X. Given that summation, the probability that q or more of any specific mutant appearing in a particular class (P(i)) is:

[[Image:Fd5_4.gif]]

The 69 promoters being examined were divided into two phenotypic classes based on their fluorescence: the top 50th percentile (brightest) and the bottom 50th percentile (dimmest). Because there are only two classes, the statistical analysis is simplified somewhat. The complete statistical analysis can be seen here in Figure 5:

[[Image:Zam0050667180002.gif]]

Figure 5. Statistical distribution of mutations and their effects on mutant fluorescence. In panel A, the vertical axis shows the mutant number, where the mutants are sorted in descending order by their relative fluorescence. In general, the single-cell fluorescence distribution for each mutant strain was log normal distributed. The horizontal axis shows the mean of the log relative fluorescence for each mutant strain, where the error is the standard deviation of this distribution. Reading to the right from panel A into panel B reveals the point mutations present in each mutant. For each location in a mutant (where location is indicated on the horizontal axis) that was changed via the error-prone PCR, a black dot is indicated. With only two exceptions, all of these changes are base transitions rather than transversions, so the sequence of each of the 69 clones can be inferred from the wild-type sequence shown in panel D. (All of the mutations indicated in panel B are transitions with the exception of one A-C transversion at –125 bp in clone 53 and one T-G transversion at –8 in clone 68. These were treated as though they were transitions in our analysis.) Reading down from panel B into panel C shows how mutations at a particular location partition between the two classes of mutants: the top and bottom 50th percentiles. Sites that have no effect on the fluorescence phenotype should partition equally between the two classes, i.e., they should follow a binomial distribution with P = 0.5. Sites that deviate from this distribution are labeled with a dot and are colored either green or red, corresponding to the apparent effect of a mutation at the site. For these sites, P values are indicated, where this value is the probability of seeing a distribution at least as skewed to one side. Sites that were subsequently tested experimentally (see text) are indicated with an asterisk, where the color of the asterisk denotes the expected effect of a mutation at the site. We chose a range of sites to test experimentally from sites with high-confidence (low P value) positive effects to those with low-confidence (P value 0.5) negative effects (Table 1). These sites are also shown in panel D, which contains the wild-type nucleotide sequence of the promoter region that was subjected to mutation. From Jensen et all (2006). Permission pending.

Statistical analysis revealed seven nucleiotide positions that were correlated with one of the two classes in a significant manner. These seven positions were then tested individually, to see if their phenotype when isolated matched their phenotype when the mutation was random (and accompanied by many other mutations).

When tested, six out of the seven mutants proved to have a similar phenotype in isolation to the phenotype they had in the random mutations, meaning that the statistical model used to predict the significant mutations was accurate and predicted correctly.

=== De Mey, Maertens, Lequeux, Soetart, and Vandamme (2007): Probability and Partial Least Squares Modeling ===

Modeling Promoter Activity

2007-12-10T16:41:03Z

WikiSysop: /* Jensen and Hammer (1997): Spacer Sequences */

== Modeling Promoter Activity ==

In order to use synthetic promoters to their fullest potential, we have to understand how they work. Sythetic promoters cannot help us model gene circuit activity unless models are developed for the activity of the promoter itself. Determining how exactly a promoter's strength correlates to its mutations is not easy, since for the most part it requires working with promoters on the level of individual sets of nucleiotides.

=== Jensen and Hammer (1997): Spacer Sequences ===

In this 1997 paper, Jensen and Hammer constructed a library of synthetic promoters based on the ''Lactococcus lactis'' prokaryotic promoter in order to better determine how gene sequence of promoters was tied to the promoter strength. Specifically, Jensen and Hammer were looking for a way to construct a constitutively active promoter – one that was always turned on, without needing an inducer – that could be safely used to tune gene expression in industrial-scale metabolic engineering projects, where inducers might be impractical or hazardous.

In order to tune the steady-state ''L. lactis'' promoter without using an inducer, Jensen and Hammer had to create a library of ''L. lactis'' mutant promoters, all with various levels of activity. To generate the library, they used the method described in [[Promoters and Reporters in Synthetic Biology]]: constructing oligonucleiotides that matched the genes common to all previous ''L. lactis'' promoters and mutants, then allowing the oligonucleiotides to be joined together by random spacer sequences.

After the promoter library was synthesized, promoters were cloned into both ''L. lactis'' and ''E. coli''; each cell culture containing a different promoter was tested for the level of beta-galactosidase activity. The activity of each promoter (in Miller units, or beta-galactosidase concentration) is described in Figure 3.

[[Image:Am0180933003.gif]] 
Figure 3. Library of synthetic promoters for L. lactis. Promoter activities (Miller units) were assayed from the expression of a reporter gene (lacLM) encoding -galactosidase transcribed from the different synthetic promoter clones on the promoter cloning vector pAK80. The patterns of the data points indicate which promoter clones contain errors in either the 35 or the 10 consensus sequence or in the length of the spacer between these sequences. From Jensen and Hammer (1997). Permission Pending.

The mutant promoters expressed a wide range of activity, increasing in small increments. Note that not all of the clones were "perfect" - a few had mutations in the oligonucleotide sequences that were supposed to be preserved across the library. Those clones are indicated in the graph above. However, their data was not removed because it was within range of the data from the perfect clones - they caused no break in the general data trend. In addition, all clones were tested to ensure that they were truly constitutive.

When the promoters were cloned into ''E. coli'', the same basic trend was observed. While the promoters did not demonstrate the same level of activity as they did in ''L. lactis'', there was still a wide range of activity observed, with the activity level increasing in steady increments.

Jensen and Hammer constructed a library of synthetic promoters that could be constitutively expressed and covered a range of activity levels, but it was still not known for certain what caused a certain promoter to be active at a certain rate. Jensen and Hammer suggested in their Discussion that "it seems that the overall three-dimensional structure which arises from a particular nucleiotide sequence could be important".

=== Jensen, Alper, Fischer, and Stephanopoulis (2006): Statistical Modeling and Critical Mutation Sites ===

In this paper, Jensen et al tried to determine exactly why some promoters in a promoter library were stronger than others, and which mutations might cause the change in strength. Jensen et al propose to examine promoter libraries statistically rather than via assays; they will determine which mutations are associated with which phenotypes based on when they appear.

Say, for example, that you are creating a mutant library of a protein that can fluoresce one of three colors: red, blue, or green. If a given point mutation – let’s call it A – has no effect on the color of the fluorescence, then (assuming the mutagenesis is truly random) that mutation should appear in every phenotype proportional to the amount of protein with that phenotype. It will not appear in one phenotype significantly more than the others unless there is significantly more protein with that phenotype. It follows, then, that if point mutation B appears much more often in, say, blue protein ''without there being much more blue protein than red or green protein'', mutation B might have some effect on the protein’s phenotype. It is probably not the sole cause of the blue color, but it is associated with it.

To test their statistical analysis, Jensen et al generated different variants of a single promoter via error-prone PCR, fused the promoter into a plasmid with a GFP reporter gene, and then measured the amount of GFP via flow cytometry. The promoters were then sequenced, and any with insertions or deletions were removed until 69 promoters remained.

Now, assume that each mutant can be classified into one of an unknown number or phenotypic (descriptive) classes; let's call that number M. So there would be n(m) mutants in each class, with the summation of n(m) equalling all hypothetical mutants. Now, say you have a set of mutated promoters of size X, where X < N, all with one particular point mutation. If that mutation has no effect on the phenotype of the promoter, then the number of mutants in any given class with that point mutation would equal X/N - the total number of those mutants divided by the total number of promoters. In other words, they would be distributed evenly.

In multinomial statiestics, the probability that any one set X will take on another set of values y is:

[[Image:Fd2_1.gif]]

Where the summation of y is equal to X. Given that summation, the probability that q or more of any specific mutant appearing in a particular class (P(i)) is:

[[Image:Fd5_4.gif]]

The 69 promoters being examined were divided into two phenotypic classes based on their fluorescence: the top 50th percentile (brightest) and the bottom 50th percentile (dimmest). Because there are only two classes, the statistical analysis is simplified somewhat. The complete statistical analysis can be seen here in Figure 5:

[[Image:Zam0050667180002.gif]]

Figure 5. Statistical distribution of mutations and their effects on mutant fluorescence. In panel A, the vertical axis shows the mutant number, where the mutants are sorted in descending order by their relative fluorescence. In general, the single-cell fluorescence distribution for each mutant strain was log normal distributed. The horizontal axis shows the mean of the log relative fluorescence for each mutant strain, where the error is the standard deviation of this distribution. Reading to the right from panel A into panel B reveals the point mutations present in each mutant. For each location in a mutant (where location is indicated on the horizontal axis) that was changed via the error-prone PCR, a black dot is indicated. With only two exceptions, all of these changes are base transitions rather than transversions, so the sequence of each of the 69 clones can be inferred from the wild-type sequence shown in panel D. (All of the mutations indicated in panel B are transitions with the exception of one A-C transversion at –125 bp in clone 53 and one T-G transversion at –8 in clone 68. These were treated as though they were transitions in our analysis.) Reading down from panel B into panel C shows how mutations at a particular location partition between the two classes of mutants: the top and bottom 50th percentiles. Sites that have no effect on the fluorescence phenotype should partition equally between the two classes, i.e., they should follow a binomial distribution with P = 0.5. Sites that deviate from this distribution are labeled with a dot and are colored either green or red, corresponding to the apparent effect of a mutation at the site. For these sites, P values are indicated, where this value is the probability of seeing a distribution at least as skewed to one side. Sites that were subsequently tested experimentally (see text) are indicated with an asterisk, where the color of the asterisk denotes the expected effect of a mutation at the site. We chose a range of sites to test experimentally from sites with high-confidence (low P value) positive effects to those with low-confidence (P value 0.5) negative effects (Table 1). These sites are also shown in panel D, which contains the wild-type nucleotide sequence of the promoter region that was subjected to mutation. From Jensen et all (2006). Permission pending.

Statistical analysis revealed seven nucleiotide positions that were correlated with one of the two classes in a significant manner. These seven positions were then tested individually, to see if their phenotype when isolated matched their phenotype when the mutation was random (and accompanied by many other mutations).

When tested, six out of the seven mutants proved to have a similar phenotype in isolation to the phenotype they had in the random mutations, meaning that the statistical model used to predict the significant mutations was accurate and predicted correctly.

=== De Mey, Maertens, Lequeux, Soetart, and Vandamme (2007): Probability and Partial Least Squares Modeling ===

Promoters and Reporters in Synthetic Biology

2007-12-10T16:37:30Z

WikiSysop: /* Synthetic, Artificial, and Mutated Promoters and Reporters */

== What Are Promoters and Reporters? ==

[http://en.wikipedia.org/wiki/Promoter Promoters] and [http://en.wikipedia.org/wiki/Reporter_gene reporters] are genetic components used in engineering gene circuits. Promoters are DNA sequences located 'upstream', or ahead, of the DNA sequences encoding genes. Promoters provide binding sites for [http://en.wikipedia.org/wiki/Transcription_factors transcription factors], small proteins that control how and whether DNA is transcribed. Transcription factors bind to promoters in order to give [http://en.wikipedia.org/wiki/RNA_polymerase RNA polymerase] a place to bind to, so that the genes can be transcribed. RNA polymerase binds to DNA and transcribes complimentary RNA from the DNA sequence so that proteins can be formed from the DNA code. If a promoter is being repressed, then transcription cannot occur, as RNA polymerase will not have a place to bind.

Reporters are not as specific as promoters; they are genes that convey some easily-identifiable and measurable characteristic when they are transcribed, such as fluorescence or beta-galactosidase proteins. Reporters are generally attached to other gene sequences so the scientist has a way of knowing if the gene is being transcribed - if the reporter is being transcribed, one can assume that the gene of interest is being transcribed as well.

== Synthetic, Artificial, and Mutated Promoters and Reporters ==

[http://pubs.acs.org/cgi-bin/article.cgi/achre4/1998/31/i03/html/ar960017f.html Directed evolution] is often used to mutate promoters or reporters in order to obtain desirable attributes. Directed evolution of a gene or protein sequence generally mutates or scrambles the sequence in question, screens it for a certain mutation (any cell not displaying the desirable phenotype is removed), and then amplifies the surviving cells so that the process can begin again. Many mutation and screening cycles can be performed, producing DNA sequences far removed from the original DNA code and increasing the likelyhood that a mutant sequence or cell will have desirable properties.

Another method is the synthesis of combinatorial promoters, as demonstrated in [http://www.nature.com/msb/journal/v3/n1/full/msb4100187.html Cox, Surette and Elowitz (2007)]. In their experiment, Cox ''et al''. designed modular sequence units corresponding to the three coding segments of a promoter gene. These segments, assembled at random, can create a diverse and new promoter library made up of fragments of existing promoters, even promoters that are unrelated. See Figure 1 for a diagram of combinatorial promoter synthesis.

In addition, promoters can be specifically synthesized based on the structure of an existing promoter, as in Jensen and Hammer (1997). In order to construct a series of synthetic promoters similar to the ''L. lactis'' promoter, Jensen and Hammer observed consensus sequences within existing ''L. lactis.'' mutants, or sequences that were found to be similar in all or most mutants, no matter how their activity rate varied. For example, the Pribnow box, consisting of the -10 sequence TATAAT and the -35 sequence TTGACA, were consistent in many prokaryotic promoters; other sequences, such as the TG sequence one base pair upstream from the -10 sequence, are more specific to ''L. lactis''. In order to generate a promoter library, Jensen and Hammer constructed oligonucleotides for the sequences that were common in ''L. lactis'' promoters. These oligonucleotides were then seperated by spacers of random sequences; promoters with different spacer sequences made up the promoter library. See Figure 2 for an illustration of the process.

=== Why use synthetic/mutated promoters and reporters? ===
Since much of synthetic biology is based on modeling genetic and molecular mechanisms before they are built, a scientist has to be able to predict how the components of a mechanism or gene circuit will work in order to predict how the whole mechanism will work. Because they have been specifically designed and selected for, synthetic promoters and reporters make gene circuit modeling much easier.

[http://www.nature.com/msb/journal/v3/n1/full/msb4100185.html Rosenfeld, Young, Alon, Swain, and Elowitz (2007)] have demonstrated that the behavior of a gene circuit can be accurately modeled based on its promoter and repressor activity, but note that in order to accurately construct their model, they needed a specific promoter and repressor gene that followed a certain pattern of behavior (specifically, a negative regulatory circuit, in which a repressor regulates its own expression, as that circuit is the simplest to model).

Of course, the noise and randomness inherent in cellular interactions mean that no promoter or reporter's activity can be perfectly predicted.

Also, synthetic promoters and reporters are useful for when a wild-type promoter or reporter is not sufficient or lacks some property necessary for a cellular mechanism to work. For example, a reporter protein such as GFP does not degrade as soon as it is produced, so in any mechanism that has to detect a transient signal, GFP would not be a useful reporter. However, a mutated GFP, which degrades faster or in the presence of a certain compound, would negate this effect. The same principle applies for reporters which are more active at lower-than-normal or higher-than-normal temperatures. See [http://www.biophysj.org/cgi/content/abstract/73/5/2782 Patterson GH et al (1997)].

== Measuring, Testing, Tuning, and Modeling Promoters and Reporters ==

*[[Modeling Promoter Activity | Modeling Promoter Activity: Developing a model for predicting promoter activity based on mutations and gene sequence.]]

*[[Modeling Reporter Activity | Modeling Reporter Activity: The kinetics of promoter protein degradation and developing a model thereof.]]

*[[Predicting Gene Circuit Activity | Predicting gene circuit activity based on promoter and reporter modeling.]]

== Figures ==
[[Image:Msb4100187-f1.jpg]] 
Figure 1. Random assembly ligation generates a diverse promoter library. Promoters can be assembled out of modular sequence units. (A) The assembled sequence of an example promoter. The 5' overhangs of each unit are shown in red. The RNA polymerase boxes (-10 and -35) are highlighted in yellow, and the predicted start site of transcription (+1) is capitalized. Operator colors are consistent throughout the figure. (B) Steps in promoter assembly and ligation into the luciferase reporter vector: promoters are assembled by mixed ligations using 1-bp or 2-bp cohesive ends, and then ligated into a luciferase reporter plasmid. (C) Luminescence measurements in 16 inducer conditions ( each of four inducers, as indicated) for the promoter shown in (A). The output levels determine promoter logic. Note that this promoter does not respond to LuxR regulation at the distal region. (D) The 48 unique units used in the library contain operators responsive to the four TFs (indicated by color) in the regions distal, core, and proximal. In Cox, Surette, and Elowitz 2007. Permission Pending.

[[Image:Am0180933001.gif]] 
Figure 2. Strategies used for cloning synthetic promoter fragments into the promoter cloning vector pAK80. (a) Double-stranded DNA fragments carrying putative promoter activities. (b) Restriction map and schematic representation of the relevant parts of the promoter cloning vector. The stippled and solid lines show the strategies used for cloning pCP1 through pCP29 and pCP30 through pCP46, respectively. (c) Restriction map of clones pCP1 through pCP29. (d) Restriction map of clones pCP30 through pCP46. Note that a number of clones have been subject to cloning artifacts and thus may have a slightly different restriction map. BI, BamHI; AII, AflII; Ss, SspI; N, NsiI (PstI compatible); Nr, NruI; Sc, ScaI; HII, HincII; P, PstI; PII, PvuII; E, EcoRI; Sa, SacI; Xh, XhoI; BII, BglII; Sm, SmaI; Xb, XbaI (not drawn to scale). In Jensen and Hammer 1997. Permission Pending.

== Works Cited ==
*Arnold FH (1997). Design by Directed Evolution. ''Acc. Chem. Res.,''31 (3). Epub 1998 February 28. [http://pubs.acs.org/cgi-bin/article.cgi/achre4/1998/31/i03/html/ar960017f.html Full Text]

*Cox III, RS, Surette MG & Elowitz MB (2007). Programming gene expression with combinatorial promoters. ''Molecular Systems Biology''3(145). Epub 2007 November 13. [http://www.nature.com/msb/journal/v3/n1/full/msb4100187.html Full Text]

*De Mey M, Maertens J, Lequeux GJ, Soetaert WK, and Vandamme EJ (2007) Construction and model-based analysis of a promoter library for ''E. coli'': an indispensable tool for metabolic engineering. ''BMC Biotechnology''7(34). Epub 2007 June.

*Jensen, PR and Hammer, K (1997). The sequence of spacers between the consensus sequences modulates the strength of prokaryotic promoters. ''Applied and Environmental Microbiology''64(1).

*Jensen, PR and Hammer, K (1997). Artificial promoters for metabolic optimization. ''Biotechnology and Bioengineering''58(2-3).

*Jensen K, Alper H, Fischer C and Stephanopoulos G (2006). Identifying functionally important mutations from phenotypically diverse sequence data. ''Applied and Environmental Microbiology''72(5).

*Leveau, JHJ and Lindow, SE (2001). Predictive and interpretive simulation of green fluorescent protein expression in reporter bacteria. ''Journal of Bacteriology''183(23). Epub 2001 September. [http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=95514 Full text]

*Miller WG, Brandl MT, Quinones B, and Lindow SE (2001). Biological sensor for sucrose availability: relative sensitivities of various reporter genes. ''Applied Environmental Microbiology''67(3).

*Patterson GH, Knobel SM, Sharif WD, Kain SR, and Piston DW (1997). Use of the green fluorescent protein and its mutants in quantitative fluorescence microscopy. ''Biophysical Journal'' 73. Epub 1998. [http://www.biophysj.org/cgi/content/abstract/73/5/2782 Abstract]

*Rosenfeld N, Young JW, Alon U, Swain PS, and Elowitz MB (2007). Accurate prediction of gene feedback circuit behavior from component properties. ''Molecular Systems Biology''3(143). Epub 2007 November 13. [http://www.nature.com/msb/journal/v3/n1/full/msb4100185.html Full Text]

* Weiss R, Basu S, Hooshangi S, Kalmbach A, Karig D, Mehreja R, and Netravali I (2003). Genetic circuit building blocks for cellular computation, communications, and signal processing. Natural Computing 2 (1). Epub 2004 November 02. [http://www.springerlink.com/content/h885l73711912672/ Abstract]

Promoters and Reporters in Synthetic Biology

2007-12-10T16:36:07Z

WikiSysop: /* Figures */

== What Are Promoters and Reporters? ==

[http://en.wikipedia.org/wiki/Promoter Promoters] and [http://en.wikipedia.org/wiki/Reporter_gene reporters] are genetic components used in engineering gene circuits. Promoters are DNA sequences located 'upstream', or ahead, of the DNA sequences encoding genes. Promoters provide binding sites for [http://en.wikipedia.org/wiki/Transcription_factors transcription factors], small proteins that control how and whether DNA is transcribed. Transcription factors bind to promoters in order to give [http://en.wikipedia.org/wiki/RNA_polymerase RNA polymerase] a place to bind to, so that the genes can be transcribed. RNA polymerase binds to DNA and transcribes complimentary RNA from the DNA sequence so that proteins can be formed from the DNA code. If a promoter is being repressed, then transcription cannot occur, as RNA polymerase will not have a place to bind.

Reporters are not as specific as promoters; they are genes that convey some easily-identifiable and measurable characteristic when they are transcribed, such as fluorescence or beta-galactosidase proteins. Reporters are generally attached to other gene sequences so the scientist has a way of knowing if the gene is being transcribed - if the reporter is being transcribed, one can assume that the gene of interest is being transcribed as well.

== Synthetic, Artificial, and Mutated Promoters and Reporters ==

[http://pubs.acs.org/cgi-bin/article.cgi/achre4/1998/31/i03/html/ar960017f.html Directed evolution] is often used to mutate promoters or reporters in order to obtain desirable attributes. Directed evolution of a gene or protein sequence generally mutates or scrambles the sequence in question, screens it for a certain mutation (any cell not displaying the desirable phenotype is removed), and then amplifies the surviving cells so that the process can begin again. Many mutation and screening cycles can be performed, producing DNA sequences far removed from the original DNA code and increasing the likelyhood that a mutant sequence or cell will have desirable properties.

Another method is the synthesis of combinatorial promoters, as demonstrated in [http://www.nature.com/msb/journal/v3/n1/full/msb4100187.html Cox, Surette and Elowitz (2007)]. In their experiment, Cox ''et al''. designed modular sequence units corresponding to the three coding segments of a promoter gene. These segments, assembled at random, can create a diverse and new promoter library made up of fragments of existing promoters, even promoters that are unrelated. See Figure 1 for a diagram of combinatorial promoter synthesis.

In addition, promoters can be specifically synthesized based on the structure of an existing promoter, as in Jensen and Hammer (1997). In order to construct a series of synthetic promoters similar to the ''L. lactis'' promoter, Jensen and Hammer observed consensus sequences within existing ''L. lactis.'' mutants, or sequences that were found to be similar in all or most mutants, no matter how their activity rate varied. For example, the Pribnow box, consisting of the -10 sequence TATAAT and the -35 sequence TTGACA, was consistent in many prokaryotic promoters; other sequences, such as the TG sequence one base pair upstream from the -10 sequence, are more specific to ''L. lactis''. In order to generate a promoter library, Jensen and Hammer constructed oligonucleotides for the sequences that were common in ''L. lactis'' promoters. These oligonucleotides were then seperated by spacers of random sequences; promoters with different spacer sequences made up the promoter library. See Figure 2 for an illustration of the process.

=== Why use synthetic/mutated promoters and reporters? ===
Since much of synthetic biology is based on modeling genetic and molecular mechanisms before they are built, a scientist has to be able to predict how the components of a mechanism or gene circuit will work in order to predict how the whole mechanism will work. Because they have been specifically designed and selected for, synthetic promoters and reporters make gene circuit modeling much easier.

[http://www.nature.com/msb/journal/v3/n1/full/msb4100185.html Rosenfeld, Young, Alon, Swain, and Elowitz (2007)] have demonstrated that the behavior of a gene circuit can be accurately modeled based on its promoter and repressor activity, but note that in order to accurately construct their model, they needed a specific promoter and repressor gene that followed a certain pattern of behavior (specifically, a negative regulatory circuit, in which a repressor regulates its own expression, as that circuit is the simplest to model).

Of course, the noise and randomness inherent in cellular interactions mean that no promoter or reporter's activity can be perfectly predicted.

Also, synthetic promoters and reporters are useful for when a wild-type promoter or reporter is not sufficient or lacks some property necessary for a cellular mechanism to work. For example, a reporter protein such as GFP does not degrade as soon as it is produced, so in any mechanism that has to detect a transient signal, GFP would not be a useful reporter. However, a mutated GFP, which degrades faster or in the presence of a certain compound, would negate this effect. The same principle applies for reporters which are more active at lower-than-normal or higher-than-normal temperatures. See [http://www.biophysj.org/cgi/content/abstract/73/5/2782 Patterson GH et al (1997)].

== Measuring, Testing, Tuning, and Modeling Promoters and Reporters ==

*[[Modeling Promoter Activity | Modeling Promoter Activity: Developing a model for predicting promoter activity based on mutations and gene sequence.]]

*[[Modeling Reporter Activity | Modeling Reporter Activity: The kinetics of promoter protein degradation and developing a model thereof.]]

*[[Predicting Gene Circuit Activity | Predicting gene circuit activity based on promoter and reporter modeling.]]

== Figures ==
[[Image:Msb4100187-f1.jpg]] 
Figure 1. Random assembly ligation generates a diverse promoter library. Promoters can be assembled out of modular sequence units. (A) The assembled sequence of an example promoter. The 5' overhangs of each unit are shown in red. The RNA polymerase boxes (-10 and -35) are highlighted in yellow, and the predicted start site of transcription (+1) is capitalized. Operator colors are consistent throughout the figure. (B) Steps in promoter assembly and ligation into the luciferase reporter vector: promoters are assembled by mixed ligations using 1-bp or 2-bp cohesive ends, and then ligated into a luciferase reporter plasmid. (C) Luminescence measurements in 16 inducer conditions ( each of four inducers, as indicated) for the promoter shown in (A). The output levels determine promoter logic. Note that this promoter does not respond to LuxR regulation at the distal region. (D) The 48 unique units used in the library contain operators responsive to the four TFs (indicated by color) in the regions distal, core, and proximal. In Cox, Surette, and Elowitz 2007. Permission Pending.

[[Image:Am0180933001.gif]] 
Figure 2. Strategies used for cloning synthetic promoter fragments into the promoter cloning vector pAK80. (a) Double-stranded DNA fragments carrying putative promoter activities. (b) Restriction map and schematic representation of the relevant parts of the promoter cloning vector. The stippled and solid lines show the strategies used for cloning pCP1 through pCP29 and pCP30 through pCP46, respectively. (c) Restriction map of clones pCP1 through pCP29. (d) Restriction map of clones pCP30 through pCP46. Note that a number of clones have been subject to cloning artifacts and thus may have a slightly different restriction map. BI, BamHI; AII, AflII; Ss, SspI; N, NsiI (PstI compatible); Nr, NruI; Sc, ScaI; HII, HincII; P, PstI; PII, PvuII; E, EcoRI; Sa, SacI; Xh, XhoI; BII, BglII; Sm, SmaI; Xb, XbaI (not drawn to scale). In Jensen and Hammer 1997. Permission Pending.

== Works Cited ==
*Arnold FH (1997). Design by Directed Evolution. ''Acc. Chem. Res.,''31 (3). Epub 1998 February 28. [http://pubs.acs.org/cgi-bin/article.cgi/achre4/1998/31/i03/html/ar960017f.html Full Text]

*Cox III, RS, Surette MG & Elowitz MB (2007). Programming gene expression with combinatorial promoters. ''Molecular Systems Biology''3(145). Epub 2007 November 13. [http://www.nature.com/msb/journal/v3/n1/full/msb4100187.html Full Text]

*De Mey M, Maertens J, Lequeux GJ, Soetaert WK, and Vandamme EJ (2007) Construction and model-based analysis of a promoter library for ''E. coli'': an indispensable tool for metabolic engineering. ''BMC Biotechnology''7(34). Epub 2007 June.

*Jensen, PR and Hammer, K (1997). The sequence of spacers between the consensus sequences modulates the strength of prokaryotic promoters. ''Applied and Environmental Microbiology''64(1).

*Jensen, PR and Hammer, K (1997). Artificial promoters for metabolic optimization. ''Biotechnology and Bioengineering''58(2-3).

*Jensen K, Alper H, Fischer C and Stephanopoulos G (2006). Identifying functionally important mutations from phenotypically diverse sequence data. ''Applied and Environmental Microbiology''72(5).

*Leveau, JHJ and Lindow, SE (2001). Predictive and interpretive simulation of green fluorescent protein expression in reporter bacteria. ''Journal of Bacteriology''183(23). Epub 2001 September. [http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=95514 Full text]

*Miller WG, Brandl MT, Quinones B, and Lindow SE (2001). Biological sensor for sucrose availability: relative sensitivities of various reporter genes. ''Applied Environmental Microbiology''67(3).

*Patterson GH, Knobel SM, Sharif WD, Kain SR, and Piston DW (1997). Use of the green fluorescent protein and its mutants in quantitative fluorescence microscopy. ''Biophysical Journal'' 73. Epub 1998. [http://www.biophysj.org/cgi/content/abstract/73/5/2782 Abstract]

*Rosenfeld N, Young JW, Alon U, Swain PS, and Elowitz MB (2007). Accurate prediction of gene feedback circuit behavior from component properties. ''Molecular Systems Biology''3(143). Epub 2007 November 13. [http://www.nature.com/msb/journal/v3/n1/full/msb4100185.html Full Text]

* Weiss R, Basu S, Hooshangi S, Kalmbach A, Karig D, Mehreja R, and Netravali I (2003). Genetic circuit building blocks for cellular computation, communications, and signal processing. Natural Computing 2 (1). Epub 2004 November 02. [http://www.springerlink.com/content/h885l73711912672/ Abstract]

Promoters and Reporters in Synthetic Biology

2007-12-10T16:35:27Z

WikiSysop: /* Synthetic, Artificial, and Mutated Promoters and Reporters */

== What Are Promoters and Reporters? ==

[http://en.wikipedia.org/wiki/Promoter Promoters] and [http://en.wikipedia.org/wiki/Reporter_gene reporters] are genetic components used in engineering gene circuits. Promoters are DNA sequences located 'upstream', or ahead, of the DNA sequences encoding genes. Promoters provide binding sites for [http://en.wikipedia.org/wiki/Transcription_factors transcription factors], small proteins that control how and whether DNA is transcribed. Transcription factors bind to promoters in order to give [http://en.wikipedia.org/wiki/RNA_polymerase RNA polymerase] a place to bind to, so that the genes can be transcribed. RNA polymerase binds to DNA and transcribes complimentary RNA from the DNA sequence so that proteins can be formed from the DNA code. If a promoter is being repressed, then transcription cannot occur, as RNA polymerase will not have a place to bind.

Reporters are not as specific as promoters; they are genes that convey some easily-identifiable and measurable characteristic when they are transcribed, such as fluorescence or beta-galactosidase proteins. Reporters are generally attached to other gene sequences so the scientist has a way of knowing if the gene is being transcribed - if the reporter is being transcribed, one can assume that the gene of interest is being transcribed as well.

== Synthetic, Artificial, and Mutated Promoters and Reporters ==

[http://pubs.acs.org/cgi-bin/article.cgi/achre4/1998/31/i03/html/ar960017f.html Directed evolution] is often used to mutate promoters or reporters in order to obtain desirable attributes. Directed evolution of a gene or protein sequence generally mutates or scrambles the sequence in question, screens it for a certain mutation (any cell not displaying the desirable phenotype is removed), and then amplifies the surviving cells so that the process can begin again. Many mutation and screening cycles can be performed, producing DNA sequences far removed from the original DNA code and increasing the likelyhood that a mutant sequence or cell will have desirable properties.

Another method is the synthesis of combinatorial promoters, as demonstrated in [http://www.nature.com/msb/journal/v3/n1/full/msb4100187.html Cox, Surette and Elowitz (2007)]. In their experiment, Cox ''et al''. designed modular sequence units corresponding to the three coding segments of a promoter gene. These segments, assembled at random, can create a diverse and new promoter library made up of fragments of existing promoters, even promoters that are unrelated. See Figure 1 for a diagram of combinatorial promoter synthesis.

In addition, promoters can be specifically synthesized based on the structure of an existing promoter, as in Jensen and Hammer (1997). In order to construct a series of synthetic promoters similar to the ''L. lactis'' promoter, Jensen and Hammer observed consensus sequences within existing ''L. lactis.'' mutants, or sequences that were found to be similar in all or most mutants, no matter how their activity rate varied. For example, the Pribnow box, consisting of the -10 sequence TATAAT and the -35 sequence TTGACA, was consistent in many prokaryotic promoters; other sequences, such as the TG sequence one base pair upstream from the -10 sequence, are more specific to ''L. lactis''. In order to generate a promoter library, Jensen and Hammer constructed oligonucleotides for the sequences that were common in ''L. lactis'' promoters. These oligonucleotides were then seperated by spacers of random sequences; promoters with different spacer sequences made up the promoter library. See Figure 2 for an illustration of the process.

=== Why use synthetic/mutated promoters and reporters? ===
Since much of synthetic biology is based on modeling genetic and molecular mechanisms before they are built, a scientist has to be able to predict how the components of a mechanism or gene circuit will work in order to predict how the whole mechanism will work. Because they have been specifically designed and selected for, synthetic promoters and reporters make gene circuit modeling much easier.

[http://www.nature.com/msb/journal/v3/n1/full/msb4100185.html Rosenfeld, Young, Alon, Swain, and Elowitz (2007)] have demonstrated that the behavior of a gene circuit can be accurately modeled based on its promoter and repressor activity, but note that in order to accurately construct their model, they needed a specific promoter and repressor gene that followed a certain pattern of behavior (specifically, a negative regulatory circuit, in which a repressor regulates its own expression, as that circuit is the simplest to model).

Of course, the noise and randomness inherent in cellular interactions mean that no promoter or reporter's activity can be perfectly predicted.

Also, synthetic promoters and reporters are useful for when a wild-type promoter or reporter is not sufficient or lacks some property necessary for a cellular mechanism to work. For example, a reporter protein such as GFP does not degrade as soon as it is produced, so in any mechanism that has to detect a transient signal, GFP would not be a useful reporter. However, a mutated GFP, which degrades faster or in the presence of a certain compound, would negate this effect. The same principle applies for reporters which are more active at lower-than-normal or higher-than-normal temperatures. See [http://www.biophysj.org/cgi/content/abstract/73/5/2782 Patterson GH et al (1997)].

== Measuring, Testing, Tuning, and Modeling Promoters and Reporters ==

*[[Modeling Promoter Activity | Modeling Promoter Activity: Developing a model for predicting promoter activity based on mutations and gene sequence.]]

*[[Modeling Reporter Activity | Modeling Reporter Activity: The kinetics of promoter protein degradation and developing a model thereof.]]

*[[Predicting Gene Circuit Activity | Predicting gene circuit activity based on promoter and reporter modeling.]]

== Figures ==
[[Image:Msb4100187-f1.jpg]]
Figure 1. Random assembly ligation generates a diverse promoter library. Promoters can be assembled out of modular sequence units. (A) The assembled sequence of an example promoter. The 5' overhangs of each unit are shown in red. The RNA polymerase boxes (-10 and -35) are highlighted in yellow, and the predicted start site of transcription (+1) is capitalized. Operator colors are consistent throughout the figure. (B) Steps in promoter assembly and ligation into the luciferase reporter vector: promoters are assembled by mixed ligations using 1-bp or 2-bp cohesive ends, and then ligated into a luciferase reporter plasmid. (C) Luminescence measurements in 16 inducer conditions ( each of four inducers, as indicated) for the promoter shown in (A). The output levels determine promoter logic. Note that this promoter does not respond to LuxR regulation at the distal region. (D) The 48 unique units used in the library contain operators responsive to the four TFs (indicated by color) in the regions distal, core, and proximal. In Cox, Surette, and Elowitz 2007. Permission Pending.

[[Image:Am0180933001.gif]]
Figure 2. Strategies used for cloning synthetic promoter fragments into the promoter cloning vector pAK80. (a) Double-stranded DNA fragments carrying putative promoter activities. (b) Restriction map and schematic representation of the relevant parts of the promoter cloning vector. The stippled and solid lines show the strategies used for cloning pCP1 through pCP29 and pCP30 through pCP46, respectively. (c) Restriction map of clones pCP1 through pCP29. (d) Restriction map of clones pCP30 through pCP46. Note that a number of clones have been subject to cloning artifacts and thus may have a slightly different restriction map. BI, BamHI; AII, AflII; Ss, SspI; N, NsiI (PstI compatible); Nr, NruI; Sc, ScaI; HII, HincII; P, PstI; PII, PvuII; E, EcoRI; Sa, SacI; Xh, XhoI; BII, BglII; Sm, SmaI; Xb, XbaI (not drawn to scale). In Jensen and Hammer 1997. Permission Pending.

== Works Cited ==
*Arnold FH (1997). Design by Directed Evolution. ''Acc. Chem. Res.,''31 (3). Epub 1998 February 28. [http://pubs.acs.org/cgi-bin/article.cgi/achre4/1998/31/i03/html/ar960017f.html Full Text]

*Cox III, RS, Surette MG & Elowitz MB (2007). Programming gene expression with combinatorial promoters. ''Molecular Systems Biology''3(145). Epub 2007 November 13. [http://www.nature.com/msb/journal/v3/n1/full/msb4100187.html Full Text]

*De Mey M, Maertens J, Lequeux GJ, Soetaert WK, and Vandamme EJ (2007) Construction and model-based analysis of a promoter library for ''E. coli'': an indispensable tool for metabolic engineering. ''BMC Biotechnology''7(34). Epub 2007 June.

*Jensen, PR and Hammer, K (1997). The sequence of spacers between the consensus sequences modulates the strength of prokaryotic promoters. ''Applied and Environmental Microbiology''64(1).

*Jensen, PR and Hammer, K (1997). Artificial promoters for metabolic optimization. ''Biotechnology and Bioengineering''58(2-3).

*Jensen K, Alper H, Fischer C and Stephanopoulos G (2006). Identifying functionally important mutations from phenotypically diverse sequence data. ''Applied and Environmental Microbiology''72(5).

*Leveau, JHJ and Lindow, SE (2001). Predictive and interpretive simulation of green fluorescent protein expression in reporter bacteria. ''Journal of Bacteriology''183(23). Epub 2001 September. [http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=95514 Full text]

*Miller WG, Brandl MT, Quinones B, and Lindow SE (2001). Biological sensor for sucrose availability: relative sensitivities of various reporter genes. ''Applied Environmental Microbiology''67(3).

*Patterson GH, Knobel SM, Sharif WD, Kain SR, and Piston DW (1997). Use of the green fluorescent protein and its mutants in quantitative fluorescence microscopy. ''Biophysical Journal'' 73. Epub 1998. [http://www.biophysj.org/cgi/content/abstract/73/5/2782 Abstract]

*Rosenfeld N, Young JW, Alon U, Swain PS, and Elowitz MB (2007). Accurate prediction of gene feedback circuit behavior from component properties. ''Molecular Systems Biology''3(143). Epub 2007 November 13. [http://www.nature.com/msb/journal/v3/n1/full/msb4100185.html Full Text]

* Weiss R, Basu S, Hooshangi S, Kalmbach A, Karig D, Mehreja R, and Netravali I (2003). Genetic circuit building blocks for cellular computation, communications, and signal processing. Natural Computing 2 (1). Epub 2004 November 02. [http://www.springerlink.com/content/h885l73711912672/ Abstract]

Promoters and Reporters in Synthetic Biology

2007-12-10T16:35:10Z

WikiSysop: /* Synthetic, Artificial, and Mutated Promoters and Reporters */

== What Are Promoters and Reporters? ==

[http://en.wikipedia.org/wiki/Promoter Promoters] and [http://en.wikipedia.org/wiki/Reporter_gene reporters] are genetic components used in engineering gene circuits. Promoters are DNA sequences located 'upstream', or ahead, of the DNA sequences encoding genes. Promoters provide binding sites for [http://en.wikipedia.org/wiki/Transcription_factors transcription factors], small proteins that control how and whether DNA is transcribed. Transcription factors bind to promoters in order to give [http://en.wikipedia.org/wiki/RNA_polymerase RNA polymerase] a place to bind to, so that the genes can be transcribed. RNA polymerase binds to DNA and transcribes complimentary RNA from the DNA sequence so that proteins can be formed from the DNA code. If a promoter is being repressed, then transcription cannot occur, as RNA polymerase will not have a place to bind.

Reporters are not as specific as promoters; they are genes that convey some easily-identifiable and measurable characteristic when they are transcribed, such as fluorescence or beta-galactosidase proteins. Reporters are generally attached to other gene sequences so the scientist has a way of knowing if the gene is being transcribed - if the reporter is being transcribed, one can assume that the gene of interest is being transcribed as well.

== Synthetic, Artificial, and Mutated Promoters and Reporters ==

[http://pubs.acs.org/cgi-bin/article.cgi/achre4/1998/31/i03/html/ar960017f.html Directed evolution] is often used to mutate promoters or reporters in order to obtain desirable attributes. Directed evolution of a gene or protein sequence generally mutates or scrambles the sequence in question, screens it for a certain mutation (any cell not displaying the desirable phenotype is removed), and then amplifies the surviving cells so that the process can begin again. Many mutation and screening cycles can be performed, producing DNA sequences far removed from the original DNA code and increasing the likelyhood that a mutant sequence or cell will have desirable properties.

Another method is the synthesis of combinatorial promoters, as demonstrated in [http://www.nature.com/msb/journal/v3/n1/full/msb4100187.html Cox, Surette and Elowitz (2007)]. In their experiment, Cox ''et al''. designed modular sequence units corresponding to the three coding segments of a promoter gene. These segments, assembled at random, can create a diverse and new promoter library made up of fragments of existing promoters, even promoters that are unrelated. See Figure 1 for a diagram of combinatorial promoter synthesis.

In addition, promoters can be specifically synthesized based on the structure of an existing promoter, as in Jensen and Hammer (1997). In order to construct a series of synthetic promoters similar to the ''L. lactis'' promoter, Jensen and Hammer observed consensus sequences within existing ''L. lactis.'' mutants, or sequences that were found to be similar in all or most mutants, no matter how their activity rate varied. For example, the Pribnow box, consisting of the -10 sequence TATAAT and the -35 sequence TTGACA, was consistent in many prokaryotic promoters; other sequences, such as the TG sequence one base pair upstream from the -10 sequence, are more specific to ''L. Lactis''. In order to generate a promoter library, Jensen and Hammer constructed oligonucleotides for the sequences that were common in ''L. lactis'' promoters. These oligonucleotides were then seperated by spacers of random sequences; promoters with different spacer sequences made up the promoter library. See Figure 2 for an illustration of the process.

=== Why use synthetic/mutated promoters and reporters? ===
Since much of synthetic biology is based on modeling genetic and molecular mechanisms before they are built, a scientist has to be able to predict how the components of a mechanism or gene circuit will work in order to predict how the whole mechanism will work. Because they have been specifically designed and selected for, synthetic promoters and reporters make gene circuit modeling much easier.

[http://www.nature.com/msb/journal/v3/n1/full/msb4100185.html Rosenfeld, Young, Alon, Swain, and Elowitz (2007)] have demonstrated that the behavior of a gene circuit can be accurately modeled based on its promoter and repressor activity, but note that in order to accurately construct their model, they needed a specific promoter and repressor gene that followed a certain pattern of behavior (specifically, a negative regulatory circuit, in which a repressor regulates its own expression, as that circuit is the simplest to model).

Of course, the noise and randomness inherent in cellular interactions mean that no promoter or reporter's activity can be perfectly predicted.

Also, synthetic promoters and reporters are useful for when a wild-type promoter or reporter is not sufficient or lacks some property necessary for a cellular mechanism to work. For example, a reporter protein such as GFP does not degrade as soon as it is produced, so in any mechanism that has to detect a transient signal, GFP would not be a useful reporter. However, a mutated GFP, which degrades faster or in the presence of a certain compound, would negate this effect. The same principle applies for reporters which are more active at lower-than-normal or higher-than-normal temperatures. See [http://www.biophysj.org/cgi/content/abstract/73/5/2782 Patterson GH et al (1997)].

== Measuring, Testing, Tuning, and Modeling Promoters and Reporters ==

*[[Modeling Promoter Activity | Modeling Promoter Activity: Developing a model for predicting promoter activity based on mutations and gene sequence.]]

*[[Modeling Reporter Activity | Modeling Reporter Activity: The kinetics of promoter protein degradation and developing a model thereof.]]

*[[Predicting Gene Circuit Activity | Predicting gene circuit activity based on promoter and reporter modeling.]]

== Figures ==
[[Image:Msb4100187-f1.jpg]]
Figure 1. Random assembly ligation generates a diverse promoter library. Promoters can be assembled out of modular sequence units. (A) The assembled sequence of an example promoter. The 5' overhangs of each unit are shown in red. The RNA polymerase boxes (-10 and -35) are highlighted in yellow, and the predicted start site of transcription (+1) is capitalized. Operator colors are consistent throughout the figure. (B) Steps in promoter assembly and ligation into the luciferase reporter vector: promoters are assembled by mixed ligations using 1-bp or 2-bp cohesive ends, and then ligated into a luciferase reporter plasmid. (C) Luminescence measurements in 16 inducer conditions ( each of four inducers, as indicated) for the promoter shown in (A). The output levels determine promoter logic. Note that this promoter does not respond to LuxR regulation at the distal region. (D) The 48 unique units used in the library contain operators responsive to the four TFs (indicated by color) in the regions distal, core, and proximal. In Cox, Surette, and Elowitz 2007. Permission Pending.

[[Image:Am0180933001.gif]]
Figure 2. Strategies used for cloning synthetic promoter fragments into the promoter cloning vector pAK80. (a) Double-stranded DNA fragments carrying putative promoter activities. (b) Restriction map and schematic representation of the relevant parts of the promoter cloning vector. The stippled and solid lines show the strategies used for cloning pCP1 through pCP29 and pCP30 through pCP46, respectively. (c) Restriction map of clones pCP1 through pCP29. (d) Restriction map of clones pCP30 through pCP46. Note that a number of clones have been subject to cloning artifacts and thus may have a slightly different restriction map. BI, BamHI; AII, AflII; Ss, SspI; N, NsiI (PstI compatible); Nr, NruI; Sc, ScaI; HII, HincII; P, PstI; PII, PvuII; E, EcoRI; Sa, SacI; Xh, XhoI; BII, BglII; Sm, SmaI; Xb, XbaI (not drawn to scale). In Jensen and Hammer 1997. Permission Pending.

== Works Cited ==
*Arnold FH (1997). Design by Directed Evolution. ''Acc. Chem. Res.,''31 (3). Epub 1998 February 28. [http://pubs.acs.org/cgi-bin/article.cgi/achre4/1998/31/i03/html/ar960017f.html Full Text]

*Cox III, RS, Surette MG & Elowitz MB (2007). Programming gene expression with combinatorial promoters. ''Molecular Systems Biology''3(145). Epub 2007 November 13. [http://www.nature.com/msb/journal/v3/n1/full/msb4100187.html Full Text]

*De Mey M, Maertens J, Lequeux GJ, Soetaert WK, and Vandamme EJ (2007) Construction and model-based analysis of a promoter library for ''E. coli'': an indispensable tool for metabolic engineering. ''BMC Biotechnology''7(34). Epub 2007 June.

*Jensen, PR and Hammer, K (1997). The sequence of spacers between the consensus sequences modulates the strength of prokaryotic promoters. ''Applied and Environmental Microbiology''64(1).

*Jensen, PR and Hammer, K (1997). Artificial promoters for metabolic optimization. ''Biotechnology and Bioengineering''58(2-3).

*Jensen K, Alper H, Fischer C and Stephanopoulos G (2006). Identifying functionally important mutations from phenotypically diverse sequence data. ''Applied and Environmental Microbiology''72(5).

*Leveau, JHJ and Lindow, SE (2001). Predictive and interpretive simulation of green fluorescent protein expression in reporter bacteria. ''Journal of Bacteriology''183(23). Epub 2001 September. [http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=95514 Full text]

*Miller WG, Brandl MT, Quinones B, and Lindow SE (2001). Biological sensor for sucrose availability: relative sensitivities of various reporter genes. ''Applied Environmental Microbiology''67(3).

*Patterson GH, Knobel SM, Sharif WD, Kain SR, and Piston DW (1997). Use of the green fluorescent protein and its mutants in quantitative fluorescence microscopy. ''Biophysical Journal'' 73. Epub 1998. [http://www.biophysj.org/cgi/content/abstract/73/5/2782 Abstract]

*Rosenfeld N, Young JW, Alon U, Swain PS, and Elowitz MB (2007). Accurate prediction of gene feedback circuit behavior from component properties. ''Molecular Systems Biology''3(143). Epub 2007 November 13. [http://www.nature.com/msb/journal/v3/n1/full/msb4100185.html Full Text]

* Weiss R, Basu S, Hooshangi S, Kalmbach A, Karig D, Mehreja R, and Netravali I (2003). Genetic circuit building blocks for cellular computation, communications, and signal processing. Natural Computing 2 (1). Epub 2004 November 02. [http://www.springerlink.com/content/h885l73711912672/ Abstract]